To the Final Goal: Can We Predict and Suggest Mutations for Protein to Develop Desired Phenotype?
Abstract Directed evolution of proteins is a good approach to develop desired phenotypes from existing proteins. Fully experimental protein evolution usually utilizes randomization of a given protein sequence by error-prone PCR or gene shuffling followed by high-throughput selection or timeconsuming...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Seo, Joo-Hyun [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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| Anmerkung: |
© The Korean Society for Biotechnology and Bioengineering and Springer-Verlag GmbH Germany, part of Springer Nature 2018 |
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| Übergeordnetes Werk: |
Enthalten in: Biotechnology and bioprocess engineering - Seoul : Society, 1996, 23(2018), 2 vom: März, Seite 134-143 |
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| Übergeordnetes Werk: |
volume:23 ; year:2018 ; number:2 ; month:03 ; pages:134-143 |
| Links: |
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| DOI / URN: |
10.1007/s12257-018-0064-4 |
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| Katalog-ID: |
SPR024567396 |
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| 520 | |a Abstract Directed evolution of proteins is a good approach to develop desired phenotypes from existing proteins. Fully experimental protein evolution usually utilizes randomization of a given protein sequence by error-prone PCR or gene shuffling followed by high-throughput selection or timeconsuming screening method. However, these random methods create mutant library full of deleterious mutations. In addition, they need high-throughput screening or selection method to search for positive clones from an enormous size of mutant library. Construction of a mutant library while retaining the original function is important for efficient protein evolution because it greatly reduces time and effort for the identification of positive mutants. Therefore, researchers have tried to reduce the size of mutant library by minimizing the occurrence of deleterious mutants. Such efforts have led to the creation of a concept of ‘small but smart library’. For this goal, neutral drift theory has been applied. Although smart library greatly reduces the library size, it is still the beyond the capacity of low-throughput assay. In parallel, computational analysis of protein structure and efforts to discriminate mutatable residues from all residues of a given protein have been consistently pursued. Accumulated knowledge of protein evolution through random mutation and selection has improved our understanding of functions of amino acids in protein structure. Protein evolution by rational design is being developed based on such understanding. In this review, we describe how the use of semi-rationally designed library rather than completely random one has impacted the overall procedure of directed evolution. We also describe efforts made to evaluate the effect of single mutation. Such efforts will bring lazy boys to the final goal - computational mutation suggestion system. | ||
| 650 | 4 | |a directed evolution |7 (dpeaa)DE-He213 | |
| 650 | 4 | |a semi-rational evolution |7 (dpeaa)DE-He213 | |
| 650 | 4 | |a neutral drift |7 (dpeaa)DE-He213 | |
| 650 | 4 | |a smart library |7 (dpeaa)DE-He213 | |
| 650 | 4 | |a rational design |7 (dpeaa)DE-He213 | |
| 700 | 1 | |a Min, Won-Ki |4 aut | |
| 700 | 1 | |a Lee, Seung-Geun |4 aut | |
| 700 | 1 | |a Yun, Hyungdon |4 aut | |
| 700 | 1 | |a Kim, Byung-Gee |4 aut | |
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| 912 | |a GBV_ILN_2027 | ||
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| 912 | |a GBV_ILN_2057 | ||
| 912 | |a GBV_ILN_2059 | ||
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| 912 | |a GBV_ILN_2064 | ||
| 912 | |a GBV_ILN_2065 | ||
| 912 | |a GBV_ILN_2068 | ||
| 912 | |a GBV_ILN_2070 | ||
| 912 | |a GBV_ILN_2086 | ||
| 912 | |a GBV_ILN_2088 | ||
| 912 | |a GBV_ILN_2093 | ||
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10.1007/s12257-018-0064-4 doi (DE-627)SPR024567396 (SPR)s12257-018-0064-4-e DE-627 ger DE-627 rakwb eng Seo, Joo-Hyun verfasserin aut To the Final Goal: Can We Predict and Suggest Mutations for Protein to Develop Desired Phenotype? 2018 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Korean Society for Biotechnology and Bioengineering and Springer-Verlag GmbH Germany, part of Springer Nature 2018 Abstract Directed evolution of proteins is a good approach to develop desired phenotypes from existing proteins. Fully experimental protein evolution usually utilizes randomization of a given protein sequence by error-prone PCR or gene shuffling followed by high-throughput selection or timeconsuming screening method. However, these random methods create mutant library full of deleterious mutations. In addition, they need high-throughput screening or selection method to search for positive clones from an enormous size of mutant library. Construction of a mutant library while retaining the original function is important for efficient protein evolution because it greatly reduces time and effort for the identification of positive mutants. Therefore, researchers have tried to reduce the size of mutant library by minimizing the occurrence of deleterious mutants. Such efforts have led to the creation of a concept of ‘small but smart library’. For this goal, neutral drift theory has been applied. Although smart library greatly reduces the library size, it is still the beyond the capacity of low-throughput assay. In parallel, computational analysis of protein structure and efforts to discriminate mutatable residues from all residues of a given protein have been consistently pursued. Accumulated knowledge of protein evolution through random mutation and selection has improved our understanding of functions of amino acids in protein structure. Protein evolution by rational design is being developed based on such understanding. In this review, we describe how the use of semi-rationally designed library rather than completely random one has impacted the overall procedure of directed evolution. We also describe efforts made to evaluate the effect of single mutation. Such efforts will bring lazy boys to the final goal - computational mutation suggestion system. directed evolution (dpeaa)DE-He213 semi-rational evolution (dpeaa)DE-He213 neutral drift (dpeaa)DE-He213 smart library (dpeaa)DE-He213 rational design (dpeaa)DE-He213 Min, Won-Ki aut Lee, Seung-Geun aut Yun, Hyungdon aut Kim, Byung-Gee aut Enthalten in Biotechnology and bioprocess engineering Seoul : Society, 1996 23(2018), 2 vom: März, Seite 134-143 (DE-627)373321821 (DE-600)2125481-3 1976-3816 nnns volume:23 year:2018 number:2 month:03 pages:134-143 https://dx.doi.org/10.1007/s12257-018-0064-4 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_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 AR 23 2018 2 03 134-143 |
| spelling |
10.1007/s12257-018-0064-4 doi (DE-627)SPR024567396 (SPR)s12257-018-0064-4-e DE-627 ger DE-627 rakwb eng Seo, Joo-Hyun verfasserin aut To the Final Goal: Can We Predict and Suggest Mutations for Protein to Develop Desired Phenotype? 2018 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Korean Society for Biotechnology and Bioengineering and Springer-Verlag GmbH Germany, part of Springer Nature 2018 Abstract Directed evolution of proteins is a good approach to develop desired phenotypes from existing proteins. Fully experimental protein evolution usually utilizes randomization of a given protein sequence by error-prone PCR or gene shuffling followed by high-throughput selection or timeconsuming screening method. However, these random methods create mutant library full of deleterious mutations. In addition, they need high-throughput screening or selection method to search for positive clones from an enormous size of mutant library. Construction of a mutant library while retaining the original function is important for efficient protein evolution because it greatly reduces time and effort for the identification of positive mutants. Therefore, researchers have tried to reduce the size of mutant library by minimizing the occurrence of deleterious mutants. Such efforts have led to the creation of a concept of ‘small but smart library’. For this goal, neutral drift theory has been applied. Although smart library greatly reduces the library size, it is still the beyond the capacity of low-throughput assay. In parallel, computational analysis of protein structure and efforts to discriminate mutatable residues from all residues of a given protein have been consistently pursued. Accumulated knowledge of protein evolution through random mutation and selection has improved our understanding of functions of amino acids in protein structure. Protein evolution by rational design is being developed based on such understanding. In this review, we describe how the use of semi-rationally designed library rather than completely random one has impacted the overall procedure of directed evolution. We also describe efforts made to evaluate the effect of single mutation. Such efforts will bring lazy boys to the final goal - computational mutation suggestion system. directed evolution (dpeaa)DE-He213 semi-rational evolution (dpeaa)DE-He213 neutral drift (dpeaa)DE-He213 smart library (dpeaa)DE-He213 rational design (dpeaa)DE-He213 Min, Won-Ki aut Lee, Seung-Geun aut Yun, Hyungdon aut Kim, Byung-Gee aut Enthalten in Biotechnology and bioprocess engineering Seoul : Society, 1996 23(2018), 2 vom: März, Seite 134-143 (DE-627)373321821 (DE-600)2125481-3 1976-3816 nnns volume:23 year:2018 number:2 month:03 pages:134-143 https://dx.doi.org/10.1007/s12257-018-0064-4 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_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 AR 23 2018 2 03 134-143 |
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10.1007/s12257-018-0064-4 doi (DE-627)SPR024567396 (SPR)s12257-018-0064-4-e DE-627 ger DE-627 rakwb eng Seo, Joo-Hyun verfasserin aut To the Final Goal: Can We Predict and Suggest Mutations for Protein to Develop Desired Phenotype? 2018 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Korean Society for Biotechnology and Bioengineering and Springer-Verlag GmbH Germany, part of Springer Nature 2018 Abstract Directed evolution of proteins is a good approach to develop desired phenotypes from existing proteins. Fully experimental protein evolution usually utilizes randomization of a given protein sequence by error-prone PCR or gene shuffling followed by high-throughput selection or timeconsuming screening method. However, these random methods create mutant library full of deleterious mutations. In addition, they need high-throughput screening or selection method to search for positive clones from an enormous size of mutant library. Construction of a mutant library while retaining the original function is important for efficient protein evolution because it greatly reduces time and effort for the identification of positive mutants. Therefore, researchers have tried to reduce the size of mutant library by minimizing the occurrence of deleterious mutants. Such efforts have led to the creation of a concept of ‘small but smart library’. For this goal, neutral drift theory has been applied. Although smart library greatly reduces the library size, it is still the beyond the capacity of low-throughput assay. In parallel, computational analysis of protein structure and efforts to discriminate mutatable residues from all residues of a given protein have been consistently pursued. Accumulated knowledge of protein evolution through random mutation and selection has improved our understanding of functions of amino acids in protein structure. Protein evolution by rational design is being developed based on such understanding. In this review, we describe how the use of semi-rationally designed library rather than completely random one has impacted the overall procedure of directed evolution. We also describe efforts made to evaluate the effect of single mutation. Such efforts will bring lazy boys to the final goal - computational mutation suggestion system. directed evolution (dpeaa)DE-He213 semi-rational evolution (dpeaa)DE-He213 neutral drift (dpeaa)DE-He213 smart library (dpeaa)DE-He213 rational design (dpeaa)DE-He213 Min, Won-Ki aut Lee, Seung-Geun aut Yun, Hyungdon aut Kim, Byung-Gee aut Enthalten in Biotechnology and bioprocess engineering Seoul : Society, 1996 23(2018), 2 vom: März, Seite 134-143 (DE-627)373321821 (DE-600)2125481-3 1976-3816 nnns volume:23 year:2018 number:2 month:03 pages:134-143 https://dx.doi.org/10.1007/s12257-018-0064-4 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_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 AR 23 2018 2 03 134-143 |
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10.1007/s12257-018-0064-4 doi (DE-627)SPR024567396 (SPR)s12257-018-0064-4-e DE-627 ger DE-627 rakwb eng Seo, Joo-Hyun verfasserin aut To the Final Goal: Can We Predict and Suggest Mutations for Protein to Develop Desired Phenotype? 2018 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Korean Society for Biotechnology and Bioengineering and Springer-Verlag GmbH Germany, part of Springer Nature 2018 Abstract Directed evolution of proteins is a good approach to develop desired phenotypes from existing proteins. Fully experimental protein evolution usually utilizes randomization of a given protein sequence by error-prone PCR or gene shuffling followed by high-throughput selection or timeconsuming screening method. However, these random methods create mutant library full of deleterious mutations. In addition, they need high-throughput screening or selection method to search for positive clones from an enormous size of mutant library. Construction of a mutant library while retaining the original function is important for efficient protein evolution because it greatly reduces time and effort for the identification of positive mutants. Therefore, researchers have tried to reduce the size of mutant library by minimizing the occurrence of deleterious mutants. Such efforts have led to the creation of a concept of ‘small but smart library’. For this goal, neutral drift theory has been applied. Although smart library greatly reduces the library size, it is still the beyond the capacity of low-throughput assay. In parallel, computational analysis of protein structure and efforts to discriminate mutatable residues from all residues of a given protein have been consistently pursued. Accumulated knowledge of protein evolution through random mutation and selection has improved our understanding of functions of amino acids in protein structure. Protein evolution by rational design is being developed based on such understanding. In this review, we describe how the use of semi-rationally designed library rather than completely random one has impacted the overall procedure of directed evolution. We also describe efforts made to evaluate the effect of single mutation. Such efforts will bring lazy boys to the final goal - computational mutation suggestion system. directed evolution (dpeaa)DE-He213 semi-rational evolution (dpeaa)DE-He213 neutral drift (dpeaa)DE-He213 smart library (dpeaa)DE-He213 rational design (dpeaa)DE-He213 Min, Won-Ki aut Lee, Seung-Geun aut Yun, Hyungdon aut Kim, Byung-Gee aut Enthalten in Biotechnology and bioprocess engineering Seoul : Society, 1996 23(2018), 2 vom: März, Seite 134-143 (DE-627)373321821 (DE-600)2125481-3 1976-3816 nnns volume:23 year:2018 number:2 month:03 pages:134-143 https://dx.doi.org/10.1007/s12257-018-0064-4 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_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 AR 23 2018 2 03 134-143 |
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10.1007/s12257-018-0064-4 doi (DE-627)SPR024567396 (SPR)s12257-018-0064-4-e DE-627 ger DE-627 rakwb eng Seo, Joo-Hyun verfasserin aut To the Final Goal: Can We Predict and Suggest Mutations for Protein to Develop Desired Phenotype? 2018 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Korean Society for Biotechnology and Bioengineering and Springer-Verlag GmbH Germany, part of Springer Nature 2018 Abstract Directed evolution of proteins is a good approach to develop desired phenotypes from existing proteins. Fully experimental protein evolution usually utilizes randomization of a given protein sequence by error-prone PCR or gene shuffling followed by high-throughput selection or timeconsuming screening method. However, these random methods create mutant library full of deleterious mutations. In addition, they need high-throughput screening or selection method to search for positive clones from an enormous size of mutant library. Construction of a mutant library while retaining the original function is important for efficient protein evolution because it greatly reduces time and effort for the identification of positive mutants. Therefore, researchers have tried to reduce the size of mutant library by minimizing the occurrence of deleterious mutants. Such efforts have led to the creation of a concept of ‘small but smart library’. For this goal, neutral drift theory has been applied. Although smart library greatly reduces the library size, it is still the beyond the capacity of low-throughput assay. In parallel, computational analysis of protein structure and efforts to discriminate mutatable residues from all residues of a given protein have been consistently pursued. Accumulated knowledge of protein evolution through random mutation and selection has improved our understanding of functions of amino acids in protein structure. Protein evolution by rational design is being developed based on such understanding. In this review, we describe how the use of semi-rationally designed library rather than completely random one has impacted the overall procedure of directed evolution. We also describe efforts made to evaluate the effect of single mutation. Such efforts will bring lazy boys to the final goal - computational mutation suggestion system. directed evolution (dpeaa)DE-He213 semi-rational evolution (dpeaa)DE-He213 neutral drift (dpeaa)DE-He213 smart library (dpeaa)DE-He213 rational design (dpeaa)DE-He213 Min, Won-Ki aut Lee, Seung-Geun aut Yun, Hyungdon aut Kim, Byung-Gee aut Enthalten in Biotechnology and bioprocess engineering Seoul : Society, 1996 23(2018), 2 vom: März, Seite 134-143 (DE-627)373321821 (DE-600)2125481-3 1976-3816 nnns volume:23 year:2018 number:2 month:03 pages:134-143 https://dx.doi.org/10.1007/s12257-018-0064-4 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_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 AR 23 2018 2 03 134-143 |
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Fully experimental protein evolution usually utilizes randomization of a given protein sequence by error-prone PCR or gene shuffling followed by high-throughput selection or timeconsuming screening method. However, these random methods create mutant library full of deleterious mutations. In addition, they need high-throughput screening or selection method to search for positive clones from an enormous size of mutant library. Construction of a mutant library while retaining the original function is important for efficient protein evolution because it greatly reduces time and effort for the identification of positive mutants. Therefore, researchers have tried to reduce the size of mutant library by minimizing the occurrence of deleterious mutants. Such efforts have led to the creation of a concept of ‘small but smart library’. For this goal, neutral drift theory has been applied. 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Seo, Joo-Hyun |
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Seo, Joo-Hyun misc directed evolution misc semi-rational evolution misc neutral drift misc smart library misc rational design To the Final Goal: Can We Predict and Suggest Mutations for Protein to Develop Desired Phenotype? |
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To the Final Goal: Can We Predict and Suggest Mutations for Protein to Develop Desired Phenotype? directed evolution (dpeaa)DE-He213 semi-rational evolution (dpeaa)DE-He213 neutral drift (dpeaa)DE-He213 smart library (dpeaa)DE-He213 rational design (dpeaa)DE-He213 |
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the final goal: can we predict and suggest mutations for protein to develop desired phenotype? |
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To the Final Goal: Can We Predict and Suggest Mutations for Protein to Develop Desired Phenotype? |
| abstract |
Abstract Directed evolution of proteins is a good approach to develop desired phenotypes from existing proteins. Fully experimental protein evolution usually utilizes randomization of a given protein sequence by error-prone PCR or gene shuffling followed by high-throughput selection or timeconsuming screening method. However, these random methods create mutant library full of deleterious mutations. In addition, they need high-throughput screening or selection method to search for positive clones from an enormous size of mutant library. Construction of a mutant library while retaining the original function is important for efficient protein evolution because it greatly reduces time and effort for the identification of positive mutants. Therefore, researchers have tried to reduce the size of mutant library by minimizing the occurrence of deleterious mutants. Such efforts have led to the creation of a concept of ‘small but smart library’. For this goal, neutral drift theory has been applied. Although smart library greatly reduces the library size, it is still the beyond the capacity of low-throughput assay. In parallel, computational analysis of protein structure and efforts to discriminate mutatable residues from all residues of a given protein have been consistently pursued. Accumulated knowledge of protein evolution through random mutation and selection has improved our understanding of functions of amino acids in protein structure. Protein evolution by rational design is being developed based on such understanding. In this review, we describe how the use of semi-rationally designed library rather than completely random one has impacted the overall procedure of directed evolution. We also describe efforts made to evaluate the effect of single mutation. Such efforts will bring lazy boys to the final goal - computational mutation suggestion system. © The Korean Society for Biotechnology and Bioengineering and Springer-Verlag GmbH Germany, part of Springer Nature 2018 |
| abstractGer |
Abstract Directed evolution of proteins is a good approach to develop desired phenotypes from existing proteins. Fully experimental protein evolution usually utilizes randomization of a given protein sequence by error-prone PCR or gene shuffling followed by high-throughput selection or timeconsuming screening method. However, these random methods create mutant library full of deleterious mutations. In addition, they need high-throughput screening or selection method to search for positive clones from an enormous size of mutant library. Construction of a mutant library while retaining the original function is important for efficient protein evolution because it greatly reduces time and effort for the identification of positive mutants. Therefore, researchers have tried to reduce the size of mutant library by minimizing the occurrence of deleterious mutants. Such efforts have led to the creation of a concept of ‘small but smart library’. For this goal, neutral drift theory has been applied. Although smart library greatly reduces the library size, it is still the beyond the capacity of low-throughput assay. In parallel, computational analysis of protein structure and efforts to discriminate mutatable residues from all residues of a given protein have been consistently pursued. Accumulated knowledge of protein evolution through random mutation and selection has improved our understanding of functions of amino acids in protein structure. Protein evolution by rational design is being developed based on such understanding. In this review, we describe how the use of semi-rationally designed library rather than completely random one has impacted the overall procedure of directed evolution. We also describe efforts made to evaluate the effect of single mutation. Such efforts will bring lazy boys to the final goal - computational mutation suggestion system. © The Korean Society for Biotechnology and Bioengineering and Springer-Verlag GmbH Germany, part of Springer Nature 2018 |
| abstract_unstemmed |
Abstract Directed evolution of proteins is a good approach to develop desired phenotypes from existing proteins. Fully experimental protein evolution usually utilizes randomization of a given protein sequence by error-prone PCR or gene shuffling followed by high-throughput selection or timeconsuming screening method. However, these random methods create mutant library full of deleterious mutations. In addition, they need high-throughput screening or selection method to search for positive clones from an enormous size of mutant library. Construction of a mutant library while retaining the original function is important for efficient protein evolution because it greatly reduces time and effort for the identification of positive mutants. Therefore, researchers have tried to reduce the size of mutant library by minimizing the occurrence of deleterious mutants. Such efforts have led to the creation of a concept of ‘small but smart library’. For this goal, neutral drift theory has been applied. Although smart library greatly reduces the library size, it is still the beyond the capacity of low-throughput assay. In parallel, computational analysis of protein structure and efforts to discriminate mutatable residues from all residues of a given protein have been consistently pursued. Accumulated knowledge of protein evolution through random mutation and selection has improved our understanding of functions of amino acids in protein structure. Protein evolution by rational design is being developed based on such understanding. In this review, we describe how the use of semi-rationally designed library rather than completely random one has impacted the overall procedure of directed evolution. We also describe efforts made to evaluate the effect of single mutation. Such efforts will bring lazy boys to the final goal - computational mutation suggestion system. © The Korean Society for Biotechnology and Bioengineering and Springer-Verlag GmbH Germany, part of Springer Nature 2018 |
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To the Final Goal: Can We Predict and Suggest Mutations for Protein to Develop Desired Phenotype? |
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https://dx.doi.org/10.1007/s12257-018-0064-4 |
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Min, Won-Ki Lee, Seung-Geun Yun, Hyungdon Kim, Byung-Gee |
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2024-07-04T01:29:16.883Z |
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| score |
7.3996086 |