Identification of Extracellular Signal of Secretory Peptides in Cyanobacterium aponinum PCC10605 by In Silico Approach

Abstract Cyanobacteria are a suitable host for sustainable production of recombinant proteins owing to low-cost raw material requirement. The major challenge in their application for the same are, they express proteins intracellularly and hence require difficult downstream processing. A way to overc...
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Autor*in:	Nandru, Rajesh [verfasserIn] Badhwar, Rahul Roy, Nilanjan Dasgupta, Santanu Nigam, Anshul

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2023

Schlagwörter:	Cyanobacteria Signal peptide Serine peptidase PSORT b Signal P Molecular docking Hex 8.0 CHARMM

Anmerkung:	© The Author(s), under exclusive licence to The National Academy of Sciences, India 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Übergeordnetes Werk:	Enthalten in: Proceedings of the National Academy of Sciences - New York, NY : Springer, 2012, 94(2023), 1 vom: 30. Sept., Seite 193-199
Übergeordnetes Werk:	volume:94 ; year:2023 ; number:1 ; day:30 ; month:09 ; pages:193-199

Links:	Volltext

DOI / URN:	10.1007/s40011-023-01517-9

Katalog-ID:	SPR054725070

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520			\|a Abstract Cyanobacteria are a suitable host for sustainable production of recombinant proteins owing to low-cost raw material requirement. The major challenge in their application for the same are, they express proteins intracellularly and hence require difficult downstream processing. A way to overcome this is to secrete the target proteins extracellularly. However, limited information is available on signal peptides involved in extracellular proteins in cyanobacteria. Therefore, we have designed a computational screening strategy to identify signaling peptides for extracellular proteins. It involves screening of whole proteome using online tools PSORTb and SignalP 5.0, known to predict the secretory peptides, then rank them based on docking/functionality and Molecular Dynamic simulation to investigate interaction between extracellular signal peptide and serine peptidase. Our screening through above online tools resulted in identification of 106 and 260 potential secretory peptides. The shortlisted peptides were subject to molecular docking using Hex 8.0 on basis of free energy, localization and net charge. Top hits were subjected to molecular dynamic simulation using CHARMM force field.
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912			\|a GBV_ILN_105
912			\|a GBV_ILN_110
912			\|a GBV_ILN_120
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912			\|a GBV_ILN_150
912			\|a GBV_ILN_151
912			\|a GBV_ILN_152
912			\|a GBV_ILN_161
912			\|a GBV_ILN_170
912			\|a GBV_ILN_171
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912			\|a GBV_ILN_213
912			\|a GBV_ILN_224
912			\|a GBV_ILN_230
912			\|a GBV_ILN_250
912			\|a GBV_ILN_281
912			\|a GBV_ILN_285
912			\|a GBV_ILN_293
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912			\|a GBV_ILN_2015
912			\|a GBV_ILN_2020
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912			\|a GBV_ILN_2061
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allfields	10.1007/s40011-023-01517-9 doi (DE-627)SPR054725070 (SPR)s40011-023-01517-9-e DE-627 ger DE-627 rakwb eng Nandru, Rajesh verfasserin aut Identification of Extracellular Signal of Secretory Peptides in Cyanobacterium aponinum PCC10605 by In Silico Approach 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to The National Academy of Sciences, India 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. Abstract Cyanobacteria are a suitable host for sustainable production of recombinant proteins owing to low-cost raw material requirement. The major challenge in their application for the same are, they express proteins intracellularly and hence require difficult downstream processing. A way to overcome this is to secrete the target proteins extracellularly. However, limited information is available on signal peptides involved in extracellular proteins in cyanobacteria. Therefore, we have designed a computational screening strategy to identify signaling peptides for extracellular proteins. It involves screening of whole proteome using online tools PSORTb and SignalP 5.0, known to predict the secretory peptides, then rank them based on docking/functionality and Molecular Dynamic simulation to investigate interaction between extracellular signal peptide and serine peptidase. Our screening through above online tools resulted in identification of 106 and 260 potential secretory peptides. The shortlisted peptides were subject to molecular docking using Hex 8.0 on basis of free energy, localization and net charge. Top hits were subjected to molecular dynamic simulation using CHARMM force field. Cyanobacteria (dpeaa)DE-He213 Signal peptide (dpeaa)DE-He213 Serine peptidase (dpeaa)DE-He213 PSORT b (dpeaa)DE-He213 Signal P (dpeaa)DE-He213 Molecular docking (dpeaa)DE-He213 Hex 8.0 (dpeaa)DE-He213 CHARMM (dpeaa)DE-He213 Badhwar, Rahul aut Roy, Nilanjan aut Dasgupta, Santanu aut Nigam, Anshul (orcid)0000-0002-4917-789X aut Enthalten in Proceedings of the National Academy of Sciences New York, NY : Springer, 2012 94(2023), 1 vom: 30. Sept., Seite 193-199 (DE-627)73921361X (DE-600)2707745-7 2250-1746 nnns volume:94 year:2023 number:1 day:30 month:09 pages:193-199 https://dx.doi.org/10.1007/s40011-023-01517-9 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 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_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 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_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 94 2023 1 30 09 193-199
spelling	10.1007/s40011-023-01517-9 doi (DE-627)SPR054725070 (SPR)s40011-023-01517-9-e DE-627 ger DE-627 rakwb eng Nandru, Rajesh verfasserin aut Identification of Extracellular Signal of Secretory Peptides in Cyanobacterium aponinum PCC10605 by In Silico Approach 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to The National Academy of Sciences, India 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. Abstract Cyanobacteria are a suitable host for sustainable production of recombinant proteins owing to low-cost raw material requirement. The major challenge in their application for the same are, they express proteins intracellularly and hence require difficult downstream processing. A way to overcome this is to secrete the target proteins extracellularly. However, limited information is available on signal peptides involved in extracellular proteins in cyanobacteria. Therefore, we have designed a computational screening strategy to identify signaling peptides for extracellular proteins. It involves screening of whole proteome using online tools PSORTb and SignalP 5.0, known to predict the secretory peptides, then rank them based on docking/functionality and Molecular Dynamic simulation to investigate interaction between extracellular signal peptide and serine peptidase. Our screening through above online tools resulted in identification of 106 and 260 potential secretory peptides. The shortlisted peptides were subject to molecular docking using Hex 8.0 on basis of free energy, localization and net charge. Top hits were subjected to molecular dynamic simulation using CHARMM force field. Cyanobacteria (dpeaa)DE-He213 Signal peptide (dpeaa)DE-He213 Serine peptidase (dpeaa)DE-He213 PSORT b (dpeaa)DE-He213 Signal P (dpeaa)DE-He213 Molecular docking (dpeaa)DE-He213 Hex 8.0 (dpeaa)DE-He213 CHARMM (dpeaa)DE-He213 Badhwar, Rahul aut Roy, Nilanjan aut Dasgupta, Santanu aut Nigam, Anshul (orcid)0000-0002-4917-789X aut Enthalten in Proceedings of the National Academy of Sciences New York, NY : Springer, 2012 94(2023), 1 vom: 30. Sept., Seite 193-199 (DE-627)73921361X (DE-600)2707745-7 2250-1746 nnns volume:94 year:2023 number:1 day:30 month:09 pages:193-199 https://dx.doi.org/10.1007/s40011-023-01517-9 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 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_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 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_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 94 2023 1 30 09 193-199
allfields_unstemmed	10.1007/s40011-023-01517-9 doi (DE-627)SPR054725070 (SPR)s40011-023-01517-9-e DE-627 ger DE-627 rakwb eng Nandru, Rajesh verfasserin aut Identification of Extracellular Signal of Secretory Peptides in Cyanobacterium aponinum PCC10605 by In Silico Approach 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to The National Academy of Sciences, India 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. Abstract Cyanobacteria are a suitable host for sustainable production of recombinant proteins owing to low-cost raw material requirement. The major challenge in their application for the same are, they express proteins intracellularly and hence require difficult downstream processing. A way to overcome this is to secrete the target proteins extracellularly. However, limited information is available on signal peptides involved in extracellular proteins in cyanobacteria. Therefore, we have designed a computational screening strategy to identify signaling peptides for extracellular proteins. It involves screening of whole proteome using online tools PSORTb and SignalP 5.0, known to predict the secretory peptides, then rank them based on docking/functionality and Molecular Dynamic simulation to investigate interaction between extracellular signal peptide and serine peptidase. Our screening through above online tools resulted in identification of 106 and 260 potential secretory peptides. The shortlisted peptides were subject to molecular docking using Hex 8.0 on basis of free energy, localization and net charge. Top hits were subjected to molecular dynamic simulation using CHARMM force field. Cyanobacteria (dpeaa)DE-He213 Signal peptide (dpeaa)DE-He213 Serine peptidase (dpeaa)DE-He213 PSORT b (dpeaa)DE-He213 Signal P (dpeaa)DE-He213 Molecular docking (dpeaa)DE-He213 Hex 8.0 (dpeaa)DE-He213 CHARMM (dpeaa)DE-He213 Badhwar, Rahul aut Roy, Nilanjan aut Dasgupta, Santanu aut Nigam, Anshul (orcid)0000-0002-4917-789X aut Enthalten in Proceedings of the National Academy of Sciences New York, NY : Springer, 2012 94(2023), 1 vom: 30. Sept., Seite 193-199 (DE-627)73921361X (DE-600)2707745-7 2250-1746 nnns volume:94 year:2023 number:1 day:30 month:09 pages:193-199 https://dx.doi.org/10.1007/s40011-023-01517-9 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 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_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 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_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 94 2023 1 30 09 193-199
allfieldsGer	10.1007/s40011-023-01517-9 doi (DE-627)SPR054725070 (SPR)s40011-023-01517-9-e DE-627 ger DE-627 rakwb eng Nandru, Rajesh verfasserin aut Identification of Extracellular Signal of Secretory Peptides in Cyanobacterium aponinum PCC10605 by In Silico Approach 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to The National Academy of Sciences, India 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. Abstract Cyanobacteria are a suitable host for sustainable production of recombinant proteins owing to low-cost raw material requirement. The major challenge in their application for the same are, they express proteins intracellularly and hence require difficult downstream processing. A way to overcome this is to secrete the target proteins extracellularly. However, limited information is available on signal peptides involved in extracellular proteins in cyanobacteria. Therefore, we have designed a computational screening strategy to identify signaling peptides for extracellular proteins. It involves screening of whole proteome using online tools PSORTb and SignalP 5.0, known to predict the secretory peptides, then rank them based on docking/functionality and Molecular Dynamic simulation to investigate interaction between extracellular signal peptide and serine peptidase. Our screening through above online tools resulted in identification of 106 and 260 potential secretory peptides. The shortlisted peptides were subject to molecular docking using Hex 8.0 on basis of free energy, localization and net charge. Top hits were subjected to molecular dynamic simulation using CHARMM force field. Cyanobacteria (dpeaa)DE-He213 Signal peptide (dpeaa)DE-He213 Serine peptidase (dpeaa)DE-He213 PSORT b (dpeaa)DE-He213 Signal P (dpeaa)DE-He213 Molecular docking (dpeaa)DE-He213 Hex 8.0 (dpeaa)DE-He213 CHARMM (dpeaa)DE-He213 Badhwar, Rahul aut Roy, Nilanjan aut Dasgupta, Santanu aut Nigam, Anshul (orcid)0000-0002-4917-789X aut Enthalten in Proceedings of the National Academy of Sciences New York, NY : Springer, 2012 94(2023), 1 vom: 30. Sept., Seite 193-199 (DE-627)73921361X (DE-600)2707745-7 2250-1746 nnns volume:94 year:2023 number:1 day:30 month:09 pages:193-199 https://dx.doi.org/10.1007/s40011-023-01517-9 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 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_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 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_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 94 2023 1 30 09 193-199
allfieldsSound	10.1007/s40011-023-01517-9 doi (DE-627)SPR054725070 (SPR)s40011-023-01517-9-e DE-627 ger DE-627 rakwb eng Nandru, Rajesh verfasserin aut Identification of Extracellular Signal of Secretory Peptides in Cyanobacterium aponinum PCC10605 by In Silico Approach 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to The National Academy of Sciences, India 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. Abstract Cyanobacteria are a suitable host for sustainable production of recombinant proteins owing to low-cost raw material requirement. The major challenge in their application for the same are, they express proteins intracellularly and hence require difficult downstream processing. A way to overcome this is to secrete the target proteins extracellularly. However, limited information is available on signal peptides involved in extracellular proteins in cyanobacteria. Therefore, we have designed a computational screening strategy to identify signaling peptides for extracellular proteins. It involves screening of whole proteome using online tools PSORTb and SignalP 5.0, known to predict the secretory peptides, then rank them based on docking/functionality and Molecular Dynamic simulation to investigate interaction between extracellular signal peptide and serine peptidase. Our screening through above online tools resulted in identification of 106 and 260 potential secretory peptides. The shortlisted peptides were subject to molecular docking using Hex 8.0 on basis of free energy, localization and net charge. Top hits were subjected to molecular dynamic simulation using CHARMM force field. Cyanobacteria (dpeaa)DE-He213 Signal peptide (dpeaa)DE-He213 Serine peptidase (dpeaa)DE-He213 PSORT b (dpeaa)DE-He213 Signal P (dpeaa)DE-He213 Molecular docking (dpeaa)DE-He213 Hex 8.0 (dpeaa)DE-He213 CHARMM (dpeaa)DE-He213 Badhwar, Rahul aut Roy, Nilanjan aut Dasgupta, Santanu aut Nigam, Anshul (orcid)0000-0002-4917-789X aut Enthalten in Proceedings of the National Academy of Sciences New York, NY : Springer, 2012 94(2023), 1 vom: 30. Sept., Seite 193-199 (DE-627)73921361X (DE-600)2707745-7 2250-1746 nnns volume:94 year:2023 number:1 day:30 month:09 pages:193-199 https://dx.doi.org/10.1007/s40011-023-01517-9 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 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_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 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_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 94 2023 1 30 09 193-199
language	English
source	Enthalten in Proceedings of the National Academy of Sciences 94(2023), 1 vom: 30. Sept., Seite 193-199 volume:94 year:2023 number:1 day:30 month:09 pages:193-199
sourceStr	Enthalten in Proceedings of the National Academy of Sciences 94(2023), 1 vom: 30. Sept., Seite 193-199 volume:94 year:2023 number:1 day:30 month:09 pages:193-199
format_phy_str_mv	Article
institution	findex.gbv.de
topic_facet	Cyanobacteria Signal peptide Serine peptidase PSORT b Signal P Molecular docking Hex 8.0 CHARMM
isfreeaccess_bool	false
container_title	Proceedings of the National Academy of Sciences
authorswithroles_txt_mv	Nandru, Rajesh @@aut@@ Badhwar, Rahul @@aut@@ Roy, Nilanjan @@aut@@ Dasgupta, Santanu @@aut@@ Nigam, Anshul @@aut@@
publishDateDaySort_date	2023-09-30T00:00:00Z
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author	Nandru, Rajesh
spellingShingle	Nandru, Rajesh misc Cyanobacteria misc Signal peptide misc Serine peptidase misc PSORT b misc Signal P misc Molecular docking misc Hex 8.0 misc CHARMM Identification of Extracellular Signal of Secretory Peptides in Cyanobacterium aponinum PCC10605 by In Silico Approach
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topic_title	Identification of Extracellular Signal of Secretory Peptides in Cyanobacterium aponinum PCC10605 by In Silico Approach Cyanobacteria (dpeaa)DE-He213 Signal peptide (dpeaa)DE-He213 Serine peptidase (dpeaa)DE-He213 PSORT b (dpeaa)DE-He213 Signal P (dpeaa)DE-He213 Molecular docking (dpeaa)DE-He213 Hex 8.0 (dpeaa)DE-He213 CHARMM (dpeaa)DE-He213
topic	misc Cyanobacteria misc Signal peptide misc Serine peptidase misc PSORT b misc Signal P misc Molecular docking misc Hex 8.0 misc CHARMM
topic_unstemmed	misc Cyanobacteria misc Signal peptide misc Serine peptidase misc PSORT b misc Signal P misc Molecular docking misc Hex 8.0 misc CHARMM
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title	Identification of Extracellular Signal of Secretory Peptides in Cyanobacterium aponinum PCC10605 by In Silico Approach
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title_full	Identification of Extracellular Signal of Secretory Peptides in Cyanobacterium aponinum PCC10605 by In Silico Approach
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author_browse	Nandru, Rajesh Badhwar, Rahul Roy, Nilanjan Dasgupta, Santanu Nigam, Anshul
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title_sort	identification of extracellular signal of secretory peptides in cyanobacterium aponinum pcc10605 by in silico approach
title_auth	Identification of Extracellular Signal of Secretory Peptides in Cyanobacterium aponinum PCC10605 by In Silico Approach
abstract	Abstract Cyanobacteria are a suitable host for sustainable production of recombinant proteins owing to low-cost raw material requirement. The major challenge in their application for the same are, they express proteins intracellularly and hence require difficult downstream processing. A way to overcome this is to secrete the target proteins extracellularly. However, limited information is available on signal peptides involved in extracellular proteins in cyanobacteria. Therefore, we have designed a computational screening strategy to identify signaling peptides for extracellular proteins. It involves screening of whole proteome using online tools PSORTb and SignalP 5.0, known to predict the secretory peptides, then rank them based on docking/functionality and Molecular Dynamic simulation to investigate interaction between extracellular signal peptide and serine peptidase. Our screening through above online tools resulted in identification of 106 and 260 potential secretory peptides. The shortlisted peptides were subject to molecular docking using Hex 8.0 on basis of free energy, localization and net charge. Top hits were subjected to molecular dynamic simulation using CHARMM force field. © The Author(s), under exclusive licence to The National Academy of Sciences, India 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.
abstractGer	Abstract Cyanobacteria are a suitable host for sustainable production of recombinant proteins owing to low-cost raw material requirement. The major challenge in their application for the same are, they express proteins intracellularly and hence require difficult downstream processing. A way to overcome this is to secrete the target proteins extracellularly. However, limited information is available on signal peptides involved in extracellular proteins in cyanobacteria. Therefore, we have designed a computational screening strategy to identify signaling peptides for extracellular proteins. It involves screening of whole proteome using online tools PSORTb and SignalP 5.0, known to predict the secretory peptides, then rank them based on docking/functionality and Molecular Dynamic simulation to investigate interaction between extracellular signal peptide and serine peptidase. Our screening through above online tools resulted in identification of 106 and 260 potential secretory peptides. The shortlisted peptides were subject to molecular docking using Hex 8.0 on basis of free energy, localization and net charge. Top hits were subjected to molecular dynamic simulation using CHARMM force field. © The Author(s), under exclusive licence to The National Academy of Sciences, India 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.
abstract_unstemmed	Abstract Cyanobacteria are a suitable host for sustainable production of recombinant proteins owing to low-cost raw material requirement. The major challenge in their application for the same are, they express proteins intracellularly and hence require difficult downstream processing. A way to overcome this is to secrete the target proteins extracellularly. However, limited information is available on signal peptides involved in extracellular proteins in cyanobacteria. Therefore, we have designed a computational screening strategy to identify signaling peptides for extracellular proteins. It involves screening of whole proteome using online tools PSORTb and SignalP 5.0, known to predict the secretory peptides, then rank them based on docking/functionality and Molecular Dynamic simulation to investigate interaction between extracellular signal peptide and serine peptidase. Our screening through above online tools resulted in identification of 106 and 260 potential secretory peptides. The shortlisted peptides were subject to molecular docking using Hex 8.0 on basis of free energy, localization and net charge. Top hits were subjected to molecular dynamic simulation using CHARMM force field. © The Author(s), under exclusive licence to The National Academy of Sciences, India 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.
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title_short	Identification of Extracellular Signal of Secretory Peptides in Cyanobacterium aponinum PCC10605 by In Silico Approach
url	https://dx.doi.org/10.1007/s40011-023-01517-9
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doi_str	10.1007/s40011-023-01517-9
up_date	2024-07-04T02:47:20.086Z
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Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">Abstract Cyanobacteria are a suitable host for sustainable production of recombinant proteins owing to low-cost raw material requirement. The major challenge in their application for the same are, they express proteins intracellularly and hence require difficult downstream processing. A way to overcome this is to secrete the target proteins extracellularly. However, limited information is available on signal peptides involved in extracellular proteins in cyanobacteria. 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Identification of Extracellular Signal of Secretory Peptides in Cyanobacterium aponinum PCC10605 by In Silico Approach

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