Identification of important interacting proteins (IIPs) in Plasmodium falciparum using large-scale interaction network analysis and in-silico knock-out studies

Background Plasmodium falciparum causes the most severe form of malaria and affects 3.2 million people annually. Due to the increasing incidence of resistance to existing drugs, there is a growing need to discover new and more effective drugs against malaria. Despite the global importance of P. falciparum, vast majority of its proteins are uncharacterized experimentally. Application of newer approaches using several “omics” data has become successful for exploring the biological interactions underlying cellular processes. Till date not many system level study has been published using P. falciparum protein protein interaction. Hence, the purpose of this study is to develop a standardized pipeline for structural, functional, and topographical analysis of large scale protein protein interaction network (PPIN) in order to identify proteins important for network topology and integrity. Here, P. falciparum PPIN has been utilized as a model for better understanding of the molecular mechanisms of survival and pathogenesis of malaria parasite. Methods Various graph theoretical approaches were implemented to identify highly interacting hub and central proteins that are crucial for network integrity. Further, potential network perturbing proteins via an in-silico knock-out (KO) analysis to isolate important interacting proteins (IIPs), which in principle, can elicit significant impact on the global and local environments of the P. falciparum interaction network. Results 177 hubs and 132 central proteins were identified from the malarial (proteins: 1607; interactions: 4750) PPI networks. Using the in-silico knock-out exercise 131 and 99 global and local network perturbing proteins were also identified. Finally, 271 proteins from P. falciparum were shortlisted as important interacting proteins (IIPs), which not only play crucial role in intra-pathogen network integrity, stage specificity but also interact with various human proteins involved in multiple metabolic pathways within the host cell. These IIPs could be used as potential drug targets in malarial research. Conclusion Graph theoretical analysis of PPIN can be a very useful approach to identify proteins that are important for regulation of the interactions required for an organism’s survival. Important interacting proteins (IIPs) identified using P. falciparum PPIN provides a useful dataset containing probable candidates for future drug target analysis. Ausführliche Beschreibung

Gespeichert in:

Autor*in:	Bhattacharyya, Madhumita [verfasserIn] Chakrabarti, Saikat

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2015

Schlagwörter:	Centrality analysis IIPs Graph theory Host pathogen interaction Hubs

Anmerkung:	© Bhattacharyya and Chakrabarti; licensee BioMed Central. 2015

Übergeordnetes Werk:	Enthalten in: Malaria journal - London : BioMed Central, 2002, 14(2015), 1 vom: 08. Feb.
Übergeordnetes Werk:	volume:14 ; year:2015 ; number:1 ; day:08 ; month:02

Links:	Volltext

DOI / URN:	10.1186/s12936-015-0562-1

Katalog-ID:	SPR02863862X

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520			\|a Background Plasmodium falciparum causes the most severe form of malaria and affects 3.2 million people annually. Due to the increasing incidence of resistance to existing drugs, there is a growing need to discover new and more effective drugs against malaria. Despite the global importance of P. falciparum, vast majority of its proteins are uncharacterized experimentally. Application of newer approaches using several “omics” data has become successful for exploring the biological interactions underlying cellular processes. Till date not many system level study has been published using P. falciparum protein protein interaction. Hence, the purpose of this study is to develop a standardized pipeline for structural, functional, and topographical analysis of large scale protein protein interaction network (PPIN) in order to identify proteins important for network topology and integrity. Here, P. falciparum PPIN has been utilized as a model for better understanding of the molecular mechanisms of survival and pathogenesis of malaria parasite. Methods Various graph theoretical approaches were implemented to identify highly interacting hub and central proteins that are crucial for network integrity. Further, potential network perturbing proteins via an in-silico knock-out (KO) analysis to isolate important interacting proteins (IIPs), which in principle, can elicit significant impact on the global and local environments of the P. falciparum interaction network. Results 177 hubs and 132 central proteins were identified from the malarial (proteins: 1607; interactions: 4750) PPI networks. Using the in-silico knock-out exercise 131 and 99 global and local network perturbing proteins were also identified. Finally, 271 proteins from P. falciparum were shortlisted as important interacting proteins (IIPs), which not only play crucial role in intra-pathogen network integrity, stage specificity but also interact with various human proteins involved in multiple metabolic pathways within the host cell. These IIPs could be used as potential drug targets in malarial research. Conclusion Graph theoretical analysis of PPIN can be a very useful approach to identify proteins that are important for regulation of the interactions required for an organism’s survival. Important interacting proteins (IIPs) identified using P. falciparum PPIN provides a useful dataset containing probable candidates for future drug target analysis.
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allfields	10.1186/s12936-015-0562-1 doi (DE-627)SPR02863862X (SPR)s12936-015-0562-1-e DE-627 ger DE-627 rakwb eng Bhattacharyya, Madhumita verfasserin aut Identification of important interacting proteins (IIPs) in Plasmodium falciparum using large-scale interaction network analysis and in-silico knock-out studies 2015 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Bhattacharyya and Chakrabarti; licensee BioMed Central. 2015 Background Plasmodium falciparum causes the most severe form of malaria and affects 3.2 million people annually. Due to the increasing incidence of resistance to existing drugs, there is a growing need to discover new and more effective drugs against malaria. Despite the global importance of P. falciparum, vast majority of its proteins are uncharacterized experimentally. Application of newer approaches using several “omics” data has become successful for exploring the biological interactions underlying cellular processes. Till date not many system level study has been published using P. falciparum protein protein interaction. Hence, the purpose of this study is to develop a standardized pipeline for structural, functional, and topographical analysis of large scale protein protein interaction network (PPIN) in order to identify proteins important for network topology and integrity. Here, P. falciparum PPIN has been utilized as a model for better understanding of the molecular mechanisms of survival and pathogenesis of malaria parasite. Methods Various graph theoretical approaches were implemented to identify highly interacting hub and central proteins that are crucial for network integrity. Further, potential network perturbing proteins via an in-silico knock-out (KO) analysis to isolate important interacting proteins (IIPs), which in principle, can elicit significant impact on the global and local environments of the P. falciparum interaction network. Results 177 hubs and 132 central proteins were identified from the malarial (proteins: 1607; interactions: 4750) PPI networks. Using the in-silico knock-out exercise 131 and 99 global and local network perturbing proteins were also identified. Finally, 271 proteins from P. falciparum were shortlisted as important interacting proteins (IIPs), which not only play crucial role in intra-pathogen network integrity, stage specificity but also interact with various human proteins involved in multiple metabolic pathways within the host cell. These IIPs could be used as potential drug targets in malarial research. Conclusion Graph theoretical analysis of PPIN can be a very useful approach to identify proteins that are important for regulation of the interactions required for an organism’s survival. Important interacting proteins (IIPs) identified using P. falciparum PPIN provides a useful dataset containing probable candidates for future drug target analysis. Centrality analysis (dpeaa)DE-He213 IIPs (dpeaa)DE-He213 Graph theory (dpeaa)DE-He213 Host pathogen interaction (dpeaa)DE-He213 Hubs (dpeaa)DE-He213 Chakrabarti, Saikat aut Enthalten in Malaria journal London : BioMed Central, 2002 14(2015), 1 vom: 08. Feb. (DE-627)355986582 (DE-600)2091229-8 1475-2875 nnns volume:14 year:2015 number:1 day:08 month:02 https://dx.doi.org/10.1186/s12936-015-0562-1 kostenfrei Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OLC-PHA GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2031 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2061 GBV_ILN_2111 GBV_ILN_2113 GBV_ILN_2190 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 14 2015 1 08 02
spelling	10.1186/s12936-015-0562-1 doi (DE-627)SPR02863862X (SPR)s12936-015-0562-1-e DE-627 ger DE-627 rakwb eng Bhattacharyya, Madhumita verfasserin aut Identification of important interacting proteins (IIPs) in Plasmodium falciparum using large-scale interaction network analysis and in-silico knock-out studies 2015 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Bhattacharyya and Chakrabarti; licensee BioMed Central. 2015 Background Plasmodium falciparum causes the most severe form of malaria and affects 3.2 million people annually. Due to the increasing incidence of resistance to existing drugs, there is a growing need to discover new and more effective drugs against malaria. Despite the global importance of P. falciparum, vast majority of its proteins are uncharacterized experimentally. Application of newer approaches using several “omics” data has become successful for exploring the biological interactions underlying cellular processes. Till date not many system level study has been published using P. falciparum protein protein interaction. Hence, the purpose of this study is to develop a standardized pipeline for structural, functional, and topographical analysis of large scale protein protein interaction network (PPIN) in order to identify proteins important for network topology and integrity. Here, P. falciparum PPIN has been utilized as a model for better understanding of the molecular mechanisms of survival and pathogenesis of malaria parasite. Methods Various graph theoretical approaches were implemented to identify highly interacting hub and central proteins that are crucial for network integrity. Further, potential network perturbing proteins via an in-silico knock-out (KO) analysis to isolate important interacting proteins (IIPs), which in principle, can elicit significant impact on the global and local environments of the P. falciparum interaction network. Results 177 hubs and 132 central proteins were identified from the malarial (proteins: 1607; interactions: 4750) PPI networks. Using the in-silico knock-out exercise 131 and 99 global and local network perturbing proteins were also identified. Finally, 271 proteins from P. falciparum were shortlisted as important interacting proteins (IIPs), which not only play crucial role in intra-pathogen network integrity, stage specificity but also interact with various human proteins involved in multiple metabolic pathways within the host cell. These IIPs could be used as potential drug targets in malarial research. Conclusion Graph theoretical analysis of PPIN can be a very useful approach to identify proteins that are important for regulation of the interactions required for an organism’s survival. Important interacting proteins (IIPs) identified using P. falciparum PPIN provides a useful dataset containing probable candidates for future drug target analysis. Centrality analysis (dpeaa)DE-He213 IIPs (dpeaa)DE-He213 Graph theory (dpeaa)DE-He213 Host pathogen interaction (dpeaa)DE-He213 Hubs (dpeaa)DE-He213 Chakrabarti, Saikat aut Enthalten in Malaria journal London : BioMed Central, 2002 14(2015), 1 vom: 08. Feb. (DE-627)355986582 (DE-600)2091229-8 1475-2875 nnns volume:14 year:2015 number:1 day:08 month:02 https://dx.doi.org/10.1186/s12936-015-0562-1 kostenfrei Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OLC-PHA GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2031 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2061 GBV_ILN_2111 GBV_ILN_2113 GBV_ILN_2190 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 14 2015 1 08 02
allfields_unstemmed	10.1186/s12936-015-0562-1 doi (DE-627)SPR02863862X (SPR)s12936-015-0562-1-e DE-627 ger DE-627 rakwb eng Bhattacharyya, Madhumita verfasserin aut Identification of important interacting proteins (IIPs) in Plasmodium falciparum using large-scale interaction network analysis and in-silico knock-out studies 2015 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Bhattacharyya and Chakrabarti; licensee BioMed Central. 2015 Background Plasmodium falciparum causes the most severe form of malaria and affects 3.2 million people annually. Due to the increasing incidence of resistance to existing drugs, there is a growing need to discover new and more effective drugs against malaria. Despite the global importance of P. falciparum, vast majority of its proteins are uncharacterized experimentally. Application of newer approaches using several “omics” data has become successful for exploring the biological interactions underlying cellular processes. Till date not many system level study has been published using P. falciparum protein protein interaction. Hence, the purpose of this study is to develop a standardized pipeline for structural, functional, and topographical analysis of large scale protein protein interaction network (PPIN) in order to identify proteins important for network topology and integrity. Here, P. falciparum PPIN has been utilized as a model for better understanding of the molecular mechanisms of survival and pathogenesis of malaria parasite. Methods Various graph theoretical approaches were implemented to identify highly interacting hub and central proteins that are crucial for network integrity. Further, potential network perturbing proteins via an in-silico knock-out (KO) analysis to isolate important interacting proteins (IIPs), which in principle, can elicit significant impact on the global and local environments of the P. falciparum interaction network. Results 177 hubs and 132 central proteins were identified from the malarial (proteins: 1607; interactions: 4750) PPI networks. Using the in-silico knock-out exercise 131 and 99 global and local network perturbing proteins were also identified. Finally, 271 proteins from P. falciparum were shortlisted as important interacting proteins (IIPs), which not only play crucial role in intra-pathogen network integrity, stage specificity but also interact with various human proteins involved in multiple metabolic pathways within the host cell. These IIPs could be used as potential drug targets in malarial research. Conclusion Graph theoretical analysis of PPIN can be a very useful approach to identify proteins that are important for regulation of the interactions required for an organism’s survival. Important interacting proteins (IIPs) identified using P. falciparum PPIN provides a useful dataset containing probable candidates for future drug target analysis. Centrality analysis (dpeaa)DE-He213 IIPs (dpeaa)DE-He213 Graph theory (dpeaa)DE-He213 Host pathogen interaction (dpeaa)DE-He213 Hubs (dpeaa)DE-He213 Chakrabarti, Saikat aut Enthalten in Malaria journal London : BioMed Central, 2002 14(2015), 1 vom: 08. Feb. (DE-627)355986582 (DE-600)2091229-8 1475-2875 nnns volume:14 year:2015 number:1 day:08 month:02 https://dx.doi.org/10.1186/s12936-015-0562-1 kostenfrei Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OLC-PHA GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2031 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2061 GBV_ILN_2111 GBV_ILN_2113 GBV_ILN_2190 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 14 2015 1 08 02
allfieldsGer	10.1186/s12936-015-0562-1 doi (DE-627)SPR02863862X (SPR)s12936-015-0562-1-e DE-627 ger DE-627 rakwb eng Bhattacharyya, Madhumita verfasserin aut Identification of important interacting proteins (IIPs) in Plasmodium falciparum using large-scale interaction network analysis and in-silico knock-out studies 2015 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Bhattacharyya and Chakrabarti; licensee BioMed Central. 2015 Background Plasmodium falciparum causes the most severe form of malaria and affects 3.2 million people annually. Due to the increasing incidence of resistance to existing drugs, there is a growing need to discover new and more effective drugs against malaria. Despite the global importance of P. falciparum, vast majority of its proteins are uncharacterized experimentally. Application of newer approaches using several “omics” data has become successful for exploring the biological interactions underlying cellular processes. Till date not many system level study has been published using P. falciparum protein protein interaction. Hence, the purpose of this study is to develop a standardized pipeline for structural, functional, and topographical analysis of large scale protein protein interaction network (PPIN) in order to identify proteins important for network topology and integrity. Here, P. falciparum PPIN has been utilized as a model for better understanding of the molecular mechanisms of survival and pathogenesis of malaria parasite. Methods Various graph theoretical approaches were implemented to identify highly interacting hub and central proteins that are crucial for network integrity. Further, potential network perturbing proteins via an in-silico knock-out (KO) analysis to isolate important interacting proteins (IIPs), which in principle, can elicit significant impact on the global and local environments of the P. falciparum interaction network. Results 177 hubs and 132 central proteins were identified from the malarial (proteins: 1607; interactions: 4750) PPI networks. Using the in-silico knock-out exercise 131 and 99 global and local network perturbing proteins were also identified. Finally, 271 proteins from P. falciparum were shortlisted as important interacting proteins (IIPs), which not only play crucial role in intra-pathogen network integrity, stage specificity but also interact with various human proteins involved in multiple metabolic pathways within the host cell. These IIPs could be used as potential drug targets in malarial research. Conclusion Graph theoretical analysis of PPIN can be a very useful approach to identify proteins that are important for regulation of the interactions required for an organism’s survival. Important interacting proteins (IIPs) identified using P. falciparum PPIN provides a useful dataset containing probable candidates for future drug target analysis. Centrality analysis (dpeaa)DE-He213 IIPs (dpeaa)DE-He213 Graph theory (dpeaa)DE-He213 Host pathogen interaction (dpeaa)DE-He213 Hubs (dpeaa)DE-He213 Chakrabarti, Saikat aut Enthalten in Malaria journal London : BioMed Central, 2002 14(2015), 1 vom: 08. Feb. (DE-627)355986582 (DE-600)2091229-8 1475-2875 nnns volume:14 year:2015 number:1 day:08 month:02 https://dx.doi.org/10.1186/s12936-015-0562-1 kostenfrei Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OLC-PHA GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2031 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2061 GBV_ILN_2111 GBV_ILN_2113 GBV_ILN_2190 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 14 2015 1 08 02
allfieldsSound	10.1186/s12936-015-0562-1 doi (DE-627)SPR02863862X (SPR)s12936-015-0562-1-e DE-627 ger DE-627 rakwb eng Bhattacharyya, Madhumita verfasserin aut Identification of important interacting proteins (IIPs) in Plasmodium falciparum using large-scale interaction network analysis and in-silico knock-out studies 2015 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Bhattacharyya and Chakrabarti; licensee BioMed Central. 2015 Background Plasmodium falciparum causes the most severe form of malaria and affects 3.2 million people annually. Due to the increasing incidence of resistance to existing drugs, there is a growing need to discover new and more effective drugs against malaria. Despite the global importance of P. falciparum, vast majority of its proteins are uncharacterized experimentally. Application of newer approaches using several “omics” data has become successful for exploring the biological interactions underlying cellular processes. Till date not many system level study has been published using P. falciparum protein protein interaction. Hence, the purpose of this study is to develop a standardized pipeline for structural, functional, and topographical analysis of large scale protein protein interaction network (PPIN) in order to identify proteins important for network topology and integrity. Here, P. falciparum PPIN has been utilized as a model for better understanding of the molecular mechanisms of survival and pathogenesis of malaria parasite. Methods Various graph theoretical approaches were implemented to identify highly interacting hub and central proteins that are crucial for network integrity. Further, potential network perturbing proteins via an in-silico knock-out (KO) analysis to isolate important interacting proteins (IIPs), which in principle, can elicit significant impact on the global and local environments of the P. falciparum interaction network. Results 177 hubs and 132 central proteins were identified from the malarial (proteins: 1607; interactions: 4750) PPI networks. Using the in-silico knock-out exercise 131 and 99 global and local network perturbing proteins were also identified. Finally, 271 proteins from P. falciparum were shortlisted as important interacting proteins (IIPs), which not only play crucial role in intra-pathogen network integrity, stage specificity but also interact with various human proteins involved in multiple metabolic pathways within the host cell. These IIPs could be used as potential drug targets in malarial research. Conclusion Graph theoretical analysis of PPIN can be a very useful approach to identify proteins that are important for regulation of the interactions required for an organism’s survival. Important interacting proteins (IIPs) identified using P. falciparum PPIN provides a useful dataset containing probable candidates for future drug target analysis. Centrality analysis (dpeaa)DE-He213 IIPs (dpeaa)DE-He213 Graph theory (dpeaa)DE-He213 Host pathogen interaction (dpeaa)DE-He213 Hubs (dpeaa)DE-He213 Chakrabarti, Saikat aut Enthalten in Malaria journal London : BioMed Central, 2002 14(2015), 1 vom: 08. Feb. (DE-627)355986582 (DE-600)2091229-8 1475-2875 nnns volume:14 year:2015 number:1 day:08 month:02 https://dx.doi.org/10.1186/s12936-015-0562-1 kostenfrei Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OLC-PHA GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2031 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2061 GBV_ILN_2111 GBV_ILN_2113 GBV_ILN_2190 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 14 2015 1 08 02
language	English
source	Enthalten in Malaria journal 14(2015), 1 vom: 08. Feb. volume:14 year:2015 number:1 day:08 month:02
sourceStr	Enthalten in Malaria journal 14(2015), 1 vom: 08. Feb. volume:14 year:2015 number:1 day:08 month:02
format_phy_str_mv	Article
institution	findex.gbv.de
topic_facet	Centrality analysis IIPs Graph theory Host pathogen interaction Hubs
isfreeaccess_bool	true
container_title	Malaria journal
authorswithroles_txt_mv	Bhattacharyya, Madhumita @@aut@@ Chakrabarti, Saikat @@aut@@
publishDateDaySort_date	2015-02-08T00:00:00Z
hierarchy_top_id	355986582
id	SPR02863862X
language_de	englisch
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author	Bhattacharyya, Madhumita
spellingShingle	Bhattacharyya, Madhumita misc Centrality analysis misc IIPs misc Graph theory misc Host pathogen interaction misc Hubs Identification of important interacting proteins (IIPs) in Plasmodium falciparum using large-scale interaction network analysis and in-silico knock-out studies
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topic_title	Identification of important interacting proteins (IIPs) in Plasmodium falciparum using large-scale interaction network analysis and in-silico knock-out studies Centrality analysis (dpeaa)DE-He213 IIPs (dpeaa)DE-He213 Graph theory (dpeaa)DE-He213 Host pathogen interaction (dpeaa)DE-He213 Hubs (dpeaa)DE-He213
topic	misc Centrality analysis misc IIPs misc Graph theory misc Host pathogen interaction misc Hubs
topic_unstemmed	misc Centrality analysis misc IIPs misc Graph theory misc Host pathogen interaction misc Hubs
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title	Identification of important interacting proteins (IIPs) in Plasmodium falciparum using large-scale interaction network analysis and in-silico knock-out studies
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title_full	Identification of important interacting proteins (IIPs) in Plasmodium falciparum using large-scale interaction network analysis and in-silico knock-out studies
author_sort	Bhattacharyya, Madhumita
journal	Malaria journal
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author_browse	Bhattacharyya, Madhumita Chakrabarti, Saikat
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format_se	Elektronische Aufsätze
author-letter	Bhattacharyya, Madhumita
doi_str_mv	10.1186/s12936-015-0562-1
title_sort	identification of important interacting proteins (iips) in plasmodium falciparum using large-scale interaction network analysis and in-silico knock-out studies
title_auth	Identification of important interacting proteins (IIPs) in Plasmodium falciparum using large-scale interaction network analysis and in-silico knock-out studies
abstract	Background Plasmodium falciparum causes the most severe form of malaria and affects 3.2 million people annually. Due to the increasing incidence of resistance to existing drugs, there is a growing need to discover new and more effective drugs against malaria. Despite the global importance of P. falciparum, vast majority of its proteins are uncharacterized experimentally. Application of newer approaches using several “omics” data has become successful for exploring the biological interactions underlying cellular processes. Till date not many system level study has been published using P. falciparum protein protein interaction. Hence, the purpose of this study is to develop a standardized pipeline for structural, functional, and topographical analysis of large scale protein protein interaction network (PPIN) in order to identify proteins important for network topology and integrity. Here, P. falciparum PPIN has been utilized as a model for better understanding of the molecular mechanisms of survival and pathogenesis of malaria parasite. Methods Various graph theoretical approaches were implemented to identify highly interacting hub and central proteins that are crucial for network integrity. Further, potential network perturbing proteins via an in-silico knock-out (KO) analysis to isolate important interacting proteins (IIPs), which in principle, can elicit significant impact on the global and local environments of the P. falciparum interaction network. Results 177 hubs and 132 central proteins were identified from the malarial (proteins: 1607; interactions: 4750) PPI networks. Using the in-silico knock-out exercise 131 and 99 global and local network perturbing proteins were also identified. Finally, 271 proteins from P. falciparum were shortlisted as important interacting proteins (IIPs), which not only play crucial role in intra-pathogen network integrity, stage specificity but also interact with various human proteins involved in multiple metabolic pathways within the host cell. These IIPs could be used as potential drug targets in malarial research. Conclusion Graph theoretical analysis of PPIN can be a very useful approach to identify proteins that are important for regulation of the interactions required for an organism’s survival. Important interacting proteins (IIPs) identified using P. falciparum PPIN provides a useful dataset containing probable candidates for future drug target analysis. © Bhattacharyya and Chakrabarti; licensee BioMed Central. 2015
abstractGer	Background Plasmodium falciparum causes the most severe form of malaria and affects 3.2 million people annually. Due to the increasing incidence of resistance to existing drugs, there is a growing need to discover new and more effective drugs against malaria. Despite the global importance of P. falciparum, vast majority of its proteins are uncharacterized experimentally. Application of newer approaches using several “omics” data has become successful for exploring the biological interactions underlying cellular processes. Till date not many system level study has been published using P. falciparum protein protein interaction. Hence, the purpose of this study is to develop a standardized pipeline for structural, functional, and topographical analysis of large scale protein protein interaction network (PPIN) in order to identify proteins important for network topology and integrity. Here, P. falciparum PPIN has been utilized as a model for better understanding of the molecular mechanisms of survival and pathogenesis of malaria parasite. Methods Various graph theoretical approaches were implemented to identify highly interacting hub and central proteins that are crucial for network integrity. Further, potential network perturbing proteins via an in-silico knock-out (KO) analysis to isolate important interacting proteins (IIPs), which in principle, can elicit significant impact on the global and local environments of the P. falciparum interaction network. Results 177 hubs and 132 central proteins were identified from the malarial (proteins: 1607; interactions: 4750) PPI networks. Using the in-silico knock-out exercise 131 and 99 global and local network perturbing proteins were also identified. Finally, 271 proteins from P. falciparum were shortlisted as important interacting proteins (IIPs), which not only play crucial role in intra-pathogen network integrity, stage specificity but also interact with various human proteins involved in multiple metabolic pathways within the host cell. These IIPs could be used as potential drug targets in malarial research. Conclusion Graph theoretical analysis of PPIN can be a very useful approach to identify proteins that are important for regulation of the interactions required for an organism’s survival. Important interacting proteins (IIPs) identified using P. falciparum PPIN provides a useful dataset containing probable candidates for future drug target analysis. © Bhattacharyya and Chakrabarti; licensee BioMed Central. 2015
abstract_unstemmed	Background Plasmodium falciparum causes the most severe form of malaria and affects 3.2 million people annually. Due to the increasing incidence of resistance to existing drugs, there is a growing need to discover new and more effective drugs against malaria. Despite the global importance of P. falciparum, vast majority of its proteins are uncharacterized experimentally. Application of newer approaches using several “omics” data has become successful for exploring the biological interactions underlying cellular processes. Till date not many system level study has been published using P. falciparum protein protein interaction. Hence, the purpose of this study is to develop a standardized pipeline for structural, functional, and topographical analysis of large scale protein protein interaction network (PPIN) in order to identify proteins important for network topology and integrity. Here, P. falciparum PPIN has been utilized as a model for better understanding of the molecular mechanisms of survival and pathogenesis of malaria parasite. Methods Various graph theoretical approaches were implemented to identify highly interacting hub and central proteins that are crucial for network integrity. Further, potential network perturbing proteins via an in-silico knock-out (KO) analysis to isolate important interacting proteins (IIPs), which in principle, can elicit significant impact on the global and local environments of the P. falciparum interaction network. Results 177 hubs and 132 central proteins were identified from the malarial (proteins: 1607; interactions: 4750) PPI networks. Using the in-silico knock-out exercise 131 and 99 global and local network perturbing proteins were also identified. Finally, 271 proteins from P. falciparum were shortlisted as important interacting proteins (IIPs), which not only play crucial role in intra-pathogen network integrity, stage specificity but also interact with various human proteins involved in multiple metabolic pathways within the host cell. These IIPs could be used as potential drug targets in malarial research. Conclusion Graph theoretical analysis of PPIN can be a very useful approach to identify proteins that are important for regulation of the interactions required for an organism’s survival. Important interacting proteins (IIPs) identified using P. falciparum PPIN provides a useful dataset containing probable candidates for future drug target analysis. © Bhattacharyya and Chakrabarti; licensee BioMed Central. 2015
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title_short	Identification of important interacting proteins (IIPs) in Plasmodium falciparum using large-scale interaction network analysis and in-silico knock-out studies
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Identification of important interacting proteins (IIPs) in Plasmodium falciparum using large-scale interaction network analysis and in-silico knock-out studies

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