A Rapid Method for Secondary-Structure Analysis of the Inulinases of Different Microbial Producers
Abstract Computer simulation of secondary structures (calculation of the ratio of α-helices, β-sheets and disordered regions) is a perspective tool needed at the initial stages of the studies of structural and functional features of inulinases, since it enables one to estimate the fluctuation ranges...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Holyavka, M. G. [verfasserIn] Artyukhov, V. G. [verfasserIn] Makin, S. M. [verfasserIn] |
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| Format: |
E-Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2018 |
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| Schlagwörter: |
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| Übergeordnetes Werk: |
Enthalten in: Biophysics - Moscow : Maik Nauka/Interperiodica, 1995, 63(2018), 1 vom: Jan., Seite 10-13 |
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| Übergeordnetes Werk: |
volume:63 ; year:2018 ; number:1 ; month:01 ; pages:10-13 |
| Links: |
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| DOI / URN: |
10.1134/S0006350918010189 |
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| Katalog-ID: |
SPR019693192 |
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| 100 | 1 | |a Holyavka, M. G. |e verfasserin |4 aut | |
| 245 | 1 | 2 | |a A Rapid Method for Secondary-Structure Analysis of the Inulinases of Different Microbial Producers |
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| 520 | |a Abstract Computer simulation of secondary structures (calculation of the ratio of α-helices, β-sheets and disordered regions) is a perspective tool needed at the initial stages of the studies of structural and functional features of inulinases, since it enables one to estimate the fluctuation ranges of tested indicators. However, the data from computations should be verified by a number of biophysical and biochemical experimental data, in particular, by experiments using IR-spectroscopy. In the present work, the difference between the experimental and computational data was 3–4% for inulinase from Aspergillus awamori and 12–18% for the enzyme from Kluyveromyces marxianus. Consequently, the analysis of secondary structures of enzymes is applicable for making rapid predictions of the fluctuation ranges of physical−chemical and kinetic characteristics of protein molecules, as well as for rapid evaluation of their dynamic state. | ||
| 650 | 4 | |a inulinase |7 (dpeaa)DE-He213 | |
| 650 | 4 | |a secondary structure |7 (dpeaa)DE-He213 | |
| 650 | 4 | |a computer analysis |7 (dpeaa)DE-He213 | |
| 700 | 1 | |a Artyukhov, V. G. |e verfasserin |4 aut | |
| 700 | 1 | |a Makin, S. M. |e verfasserin |4 aut | |
| 773 | 0 | 8 | |i Enthalten in |t Biophysics |d Moscow : Maik Nauka/Interperiodica, 1995 |g 63(2018), 1 vom: Jan., Seite 10-13 |w (DE-627)32063955X |w (DE-600)2024886-6 |x 1555-6654 |7 nnns |
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| 856 | 4 | 0 | |u https://dx.doi.org/10.1134/S0006350918010189 |z lizenzpflichtig |3 Volltext |
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| 912 | |a GBV_ILN_74 | ||
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| 912 | |a GBV_ILN_100 | ||
| 912 | |a GBV_ILN_101 | ||
| 912 | |a GBV_ILN_105 | ||
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| 912 | |a GBV_ILN_2020 | ||
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| 912 | |a GBV_ILN_2026 | ||
| 912 | |a GBV_ILN_2027 | ||
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| 912 | |a GBV_ILN_2034 | ||
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| 912 | |a GBV_ILN_2039 | ||
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| 912 | |a GBV_ILN_2048 | ||
| 912 | |a GBV_ILN_2049 | ||
| 912 | |a GBV_ILN_2050 | ||
| 912 | |a GBV_ILN_2055 | ||
| 912 | |a GBV_ILN_2057 | ||
| 912 | |a GBV_ILN_2059 | ||
| 912 | |a GBV_ILN_2061 | ||
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| 912 | |a GBV_ILN_2068 | ||
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| 912 | |a GBV_ILN_2088 | ||
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| 912 | |a GBV_ILN_2106 | ||
| 912 | |a GBV_ILN_2107 | ||
| 912 | |a GBV_ILN_2108 | ||
| 912 | |a GBV_ILN_2110 | ||
| 912 | |a GBV_ILN_2111 | ||
| 912 | |a GBV_ILN_2112 | ||
| 912 | |a GBV_ILN_2113 | ||
| 912 | |a GBV_ILN_2116 | ||
| 912 | |a GBV_ILN_2118 | ||
| 912 | |a GBV_ILN_2119 | ||
| 912 | |a GBV_ILN_2122 | ||
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| 912 | |a GBV_ILN_2143 | ||
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| 912 | |a GBV_ILN_4249 | ||
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| 912 | |a GBV_ILN_4306 | ||
| 912 | |a GBV_ILN_4307 | ||
| 912 | |a GBV_ILN_4313 | ||
| 912 | |a GBV_ILN_4322 | ||
| 912 | |a GBV_ILN_4323 | ||
| 912 | |a GBV_ILN_4324 | ||
| 912 | |a GBV_ILN_4325 | ||
| 912 | |a GBV_ILN_4326 | ||
| 912 | |a GBV_ILN_4333 | ||
| 912 | |a GBV_ILN_4334 | ||
| 912 | |a GBV_ILN_4335 | ||
| 912 | |a GBV_ILN_4336 | ||
| 912 | |a GBV_ILN_4338 | ||
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10.1134/S0006350918010189 doi (DE-627)SPR019693192 (SPR)S0006350918010189-e DE-627 ger DE-627 rakwb eng 570 530 ASE 42.12 bkl Holyavka, M. G. verfasserin aut A Rapid Method for Secondary-Structure Analysis of the Inulinases of Different Microbial Producers 2018 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract Computer simulation of secondary structures (calculation of the ratio of α-helices, β-sheets and disordered regions) is a perspective tool needed at the initial stages of the studies of structural and functional features of inulinases, since it enables one to estimate the fluctuation ranges of tested indicators. However, the data from computations should be verified by a number of biophysical and biochemical experimental data, in particular, by experiments using IR-spectroscopy. In the present work, the difference between the experimental and computational data was 3–4% for inulinase from Aspergillus awamori and 12–18% for the enzyme from Kluyveromyces marxianus. Consequently, the analysis of secondary structures of enzymes is applicable for making rapid predictions of the fluctuation ranges of physical−chemical and kinetic characteristics of protein molecules, as well as for rapid evaluation of their dynamic state. inulinase (dpeaa)DE-He213 secondary structure (dpeaa)DE-He213 computer analysis (dpeaa)DE-He213 Artyukhov, V. G. verfasserin aut Makin, S. M. verfasserin aut Enthalten in Biophysics Moscow : Maik Nauka/Interperiodica, 1995 63(2018), 1 vom: Jan., Seite 10-13 (DE-627)32063955X (DE-600)2024886-6 1555-6654 nnns volume:63 year:2018 number:1 month:01 pages:10-13 https://dx.doi.org/10.1134/S0006350918010189 lizenzpflichtig 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_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_101 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_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2070 GBV_ILN_2086 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_2116 GBV_ILN_2118 GBV_ILN_2119 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_2190 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_4012 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_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 42.12 ASE AR 63 2018 1 01 10-13 |
| spelling |
10.1134/S0006350918010189 doi (DE-627)SPR019693192 (SPR)S0006350918010189-e DE-627 ger DE-627 rakwb eng 570 530 ASE 42.12 bkl Holyavka, M. G. verfasserin aut A Rapid Method for Secondary-Structure Analysis of the Inulinases of Different Microbial Producers 2018 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract Computer simulation of secondary structures (calculation of the ratio of α-helices, β-sheets and disordered regions) is a perspective tool needed at the initial stages of the studies of structural and functional features of inulinases, since it enables one to estimate the fluctuation ranges of tested indicators. However, the data from computations should be verified by a number of biophysical and biochemical experimental data, in particular, by experiments using IR-spectroscopy. In the present work, the difference between the experimental and computational data was 3–4% for inulinase from Aspergillus awamori and 12–18% for the enzyme from Kluyveromyces marxianus. Consequently, the analysis of secondary structures of enzymes is applicable for making rapid predictions of the fluctuation ranges of physical−chemical and kinetic characteristics of protein molecules, as well as for rapid evaluation of their dynamic state. inulinase (dpeaa)DE-He213 secondary structure (dpeaa)DE-He213 computer analysis (dpeaa)DE-He213 Artyukhov, V. G. verfasserin aut Makin, S. M. verfasserin aut Enthalten in Biophysics Moscow : Maik Nauka/Interperiodica, 1995 63(2018), 1 vom: Jan., Seite 10-13 (DE-627)32063955X (DE-600)2024886-6 1555-6654 nnns volume:63 year:2018 number:1 month:01 pages:10-13 https://dx.doi.org/10.1134/S0006350918010189 lizenzpflichtig 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_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_101 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_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2070 GBV_ILN_2086 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_2116 GBV_ILN_2118 GBV_ILN_2119 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_2190 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_4012 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_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 42.12 ASE AR 63 2018 1 01 10-13 |
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10.1134/S0006350918010189 doi (DE-627)SPR019693192 (SPR)S0006350918010189-e DE-627 ger DE-627 rakwb eng 570 530 ASE 42.12 bkl Holyavka, M. G. verfasserin aut A Rapid Method for Secondary-Structure Analysis of the Inulinases of Different Microbial Producers 2018 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract Computer simulation of secondary structures (calculation of the ratio of α-helices, β-sheets and disordered regions) is a perspective tool needed at the initial stages of the studies of structural and functional features of inulinases, since it enables one to estimate the fluctuation ranges of tested indicators. However, the data from computations should be verified by a number of biophysical and biochemical experimental data, in particular, by experiments using IR-spectroscopy. In the present work, the difference between the experimental and computational data was 3–4% for inulinase from Aspergillus awamori and 12–18% for the enzyme from Kluyveromyces marxianus. Consequently, the analysis of secondary structures of enzymes is applicable for making rapid predictions of the fluctuation ranges of physical−chemical and kinetic characteristics of protein molecules, as well as for rapid evaluation of their dynamic state. inulinase (dpeaa)DE-He213 secondary structure (dpeaa)DE-He213 computer analysis (dpeaa)DE-He213 Artyukhov, V. G. verfasserin aut Makin, S. M. verfasserin aut Enthalten in Biophysics Moscow : Maik Nauka/Interperiodica, 1995 63(2018), 1 vom: Jan., Seite 10-13 (DE-627)32063955X (DE-600)2024886-6 1555-6654 nnns volume:63 year:2018 number:1 month:01 pages:10-13 https://dx.doi.org/10.1134/S0006350918010189 lizenzpflichtig 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_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_101 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_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2070 GBV_ILN_2086 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_2116 GBV_ILN_2118 GBV_ILN_2119 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_2190 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_4012 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_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 42.12 ASE AR 63 2018 1 01 10-13 |
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10.1134/S0006350918010189 doi (DE-627)SPR019693192 (SPR)S0006350918010189-e DE-627 ger DE-627 rakwb eng 570 530 ASE 42.12 bkl Holyavka, M. G. verfasserin aut A Rapid Method for Secondary-Structure Analysis of the Inulinases of Different Microbial Producers 2018 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract Computer simulation of secondary structures (calculation of the ratio of α-helices, β-sheets and disordered regions) is a perspective tool needed at the initial stages of the studies of structural and functional features of inulinases, since it enables one to estimate the fluctuation ranges of tested indicators. However, the data from computations should be verified by a number of biophysical and biochemical experimental data, in particular, by experiments using IR-spectroscopy. In the present work, the difference between the experimental and computational data was 3–4% for inulinase from Aspergillus awamori and 12–18% for the enzyme from Kluyveromyces marxianus. Consequently, the analysis of secondary structures of enzymes is applicable for making rapid predictions of the fluctuation ranges of physical−chemical and kinetic characteristics of protein molecules, as well as for rapid evaluation of their dynamic state. inulinase (dpeaa)DE-He213 secondary structure (dpeaa)DE-He213 computer analysis (dpeaa)DE-He213 Artyukhov, V. G. verfasserin aut Makin, S. M. verfasserin aut Enthalten in Biophysics Moscow : Maik Nauka/Interperiodica, 1995 63(2018), 1 vom: Jan., Seite 10-13 (DE-627)32063955X (DE-600)2024886-6 1555-6654 nnns volume:63 year:2018 number:1 month:01 pages:10-13 https://dx.doi.org/10.1134/S0006350918010189 lizenzpflichtig 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_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_101 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_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2070 GBV_ILN_2086 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_2116 GBV_ILN_2118 GBV_ILN_2119 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_2190 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_4012 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_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 42.12 ASE AR 63 2018 1 01 10-13 |
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10.1134/S0006350918010189 doi (DE-627)SPR019693192 (SPR)S0006350918010189-e DE-627 ger DE-627 rakwb eng 570 530 ASE 42.12 bkl Holyavka, M. G. verfasserin aut A Rapid Method for Secondary-Structure Analysis of the Inulinases of Different Microbial Producers 2018 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract Computer simulation of secondary structures (calculation of the ratio of α-helices, β-sheets and disordered regions) is a perspective tool needed at the initial stages of the studies of structural and functional features of inulinases, since it enables one to estimate the fluctuation ranges of tested indicators. However, the data from computations should be verified by a number of biophysical and biochemical experimental data, in particular, by experiments using IR-spectroscopy. In the present work, the difference between the experimental and computational data was 3–4% for inulinase from Aspergillus awamori and 12–18% for the enzyme from Kluyveromyces marxianus. Consequently, the analysis of secondary structures of enzymes is applicable for making rapid predictions of the fluctuation ranges of physical−chemical and kinetic characteristics of protein molecules, as well as for rapid evaluation of their dynamic state. inulinase (dpeaa)DE-He213 secondary structure (dpeaa)DE-He213 computer analysis (dpeaa)DE-He213 Artyukhov, V. G. verfasserin aut Makin, S. M. verfasserin aut Enthalten in Biophysics Moscow : Maik Nauka/Interperiodica, 1995 63(2018), 1 vom: Jan., Seite 10-13 (DE-627)32063955X (DE-600)2024886-6 1555-6654 nnns volume:63 year:2018 number:1 month:01 pages:10-13 https://dx.doi.org/10.1134/S0006350918010189 lizenzpflichtig 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_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_101 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_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2070 GBV_ILN_2086 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_2116 GBV_ILN_2118 GBV_ILN_2119 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_2190 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_4012 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_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 42.12 ASE AR 63 2018 1 01 10-13 |
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| author |
Holyavka, M. G. |
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Holyavka, M. G. ddc 570 bkl 42.12 misc inulinase misc secondary structure misc computer analysis A Rapid Method for Secondary-Structure Analysis of the Inulinases of Different Microbial Producers |
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570 530 ASE 42.12 bkl A Rapid Method for Secondary-Structure Analysis of the Inulinases of Different Microbial Producers inulinase (dpeaa)DE-He213 secondary structure (dpeaa)DE-He213 computer analysis (dpeaa)DE-He213 |
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ddc 570 bkl 42.12 misc inulinase misc secondary structure misc computer analysis |
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A Rapid Method for Secondary-Structure Analysis of the Inulinases of Different Microbial Producers |
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A Rapid Method for Secondary-Structure Analysis of the Inulinases of Different Microbial Producers |
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Holyavka, M. G. |
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rapid method for secondary-structure analysis of the inulinases of different microbial producers |
| title_auth |
A Rapid Method for Secondary-Structure Analysis of the Inulinases of Different Microbial Producers |
| abstract |
Abstract Computer simulation of secondary structures (calculation of the ratio of α-helices, β-sheets and disordered regions) is a perspective tool needed at the initial stages of the studies of structural and functional features of inulinases, since it enables one to estimate the fluctuation ranges of tested indicators. However, the data from computations should be verified by a number of biophysical and biochemical experimental data, in particular, by experiments using IR-spectroscopy. In the present work, the difference between the experimental and computational data was 3–4% for inulinase from Aspergillus awamori and 12–18% for the enzyme from Kluyveromyces marxianus. Consequently, the analysis of secondary structures of enzymes is applicable for making rapid predictions of the fluctuation ranges of physical−chemical and kinetic characteristics of protein molecules, as well as for rapid evaluation of their dynamic state. |
| abstractGer |
Abstract Computer simulation of secondary structures (calculation of the ratio of α-helices, β-sheets and disordered regions) is a perspective tool needed at the initial stages of the studies of structural and functional features of inulinases, since it enables one to estimate the fluctuation ranges of tested indicators. However, the data from computations should be verified by a number of biophysical and biochemical experimental data, in particular, by experiments using IR-spectroscopy. In the present work, the difference between the experimental and computational data was 3–4% for inulinase from Aspergillus awamori and 12–18% for the enzyme from Kluyveromyces marxianus. Consequently, the analysis of secondary structures of enzymes is applicable for making rapid predictions of the fluctuation ranges of physical−chemical and kinetic characteristics of protein molecules, as well as for rapid evaluation of their dynamic state. |
| abstract_unstemmed |
Abstract Computer simulation of secondary structures (calculation of the ratio of α-helices, β-sheets and disordered regions) is a perspective tool needed at the initial stages of the studies of structural and functional features of inulinases, since it enables one to estimate the fluctuation ranges of tested indicators. However, the data from computations should be verified by a number of biophysical and biochemical experimental data, in particular, by experiments using IR-spectroscopy. In the present work, the difference between the experimental and computational data was 3–4% for inulinase from Aspergillus awamori and 12–18% for the enzyme from Kluyveromyces marxianus. Consequently, the analysis of secondary structures of enzymes is applicable for making rapid predictions of the fluctuation ranges of physical−chemical and kinetic characteristics of protein molecules, as well as for rapid evaluation of their dynamic state. |
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| title_short |
A Rapid Method for Secondary-Structure Analysis of the Inulinases of Different Microbial Producers |
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https://dx.doi.org/10.1134/S0006350918010189 |
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Artyukhov, V. G. Makin, S. M. |
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Artyukhov, V. G. Makin, S. M. |
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| doi_str |
10.1134/S0006350918010189 |
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2024-07-04T02:36:03.034Z |
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code="a">GBV_ILN_4393</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4700</subfield></datafield><datafield tag="936" ind1="b" ind2="k"><subfield code="a">42.12</subfield><subfield code="q">ASE</subfield></datafield><datafield tag="951" ind1=" " ind2=" "><subfield code="a">AR</subfield></datafield><datafield tag="952" ind1=" " ind2=" "><subfield code="d">63</subfield><subfield code="j">2018</subfield><subfield code="e">1</subfield><subfield code="c">01</subfield><subfield code="h">10-13</subfield></datafield></record></collection>
|
| score |
7.401127 |