PySupercharge: a python algorithm for enabling ABC transporter bacterial secretion of all proteins through amino acid mutation
Background The process of producing proteins in bacterial systems and secreting them through ATP-binding cassette (ABC) transporters is an area that has been actively researched and used due to its high protein production capacity and efficiency. However, some proteins are unable to pass through the...
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| Vydáno v: | Microbial cell factories Ročník 23; číslo 1; s. 115 - 9 |
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| Hlavní autoři: | , , , , |
| Médium: | Journal Article |
| Jazyk: | angličtina |
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BioMed Central
20.04.2024
BioMed Central Ltd BMC |
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| ISSN: | 1475-2859, 1475-2859 |
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| Abstract | Background
The process of producing proteins in bacterial systems and secreting them through ATP-binding cassette (ABC) transporters is an area that has been actively researched and used due to its high protein production capacity and efficiency. However, some proteins are unable to pass through the ABC transporter after synthesis, a phenomenon we previously determined to be caused by an excessive positive charge in certain regions of their amino acid sequence. If such an excessive charge is removed, the secretion of any protein through ABC transporters becomes possible.
Results
In this study, we introduce ‘linear charge density’ as the criteria for possibility of protein secretion through ABC transporters and confirm that this criterion can be applied to various non-secretable proteins, such as SARS-CoV-2 spike proteins, botulinum toxin light chain, and human growth factors. Additionally, we develop a new algorithm, PySupercharge, that enables the secretion of proteins containing regions with high linear charge density. It selectively converts positively charged amino acids into negatively charged or neutral amino acids after linear charge density analysis to enable protein secretion through ABC transporters.
Conclusions
PySupercharge, which also minimizes functional/structural stability loss of the pre-mutation proteins through the use of sequence conservation data, is currently being operated on an accessible web server. We verified the efficacy of PySupercharge-driven protein supercharging by secreting various previously non-secretable proteins commonly used in research, and so suggest this tool for use in future research requiring effective protein production. |
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| AbstractList | The process of producing proteins in bacterial systems and secreting them through ATP-binding cassette (ABC) transporters is an area that has been actively researched and used due to its high protein production capacity and efficiency. However, some proteins are unable to pass through the ABC transporter after synthesis, a phenomenon we previously determined to be caused by an excessive positive charge in certain regions of their amino acid sequence. If such an excessive charge is removed, the secretion of any protein through ABC transporters becomes possible.BACKGROUNDThe process of producing proteins in bacterial systems and secreting them through ATP-binding cassette (ABC) transporters is an area that has been actively researched and used due to its high protein production capacity and efficiency. However, some proteins are unable to pass through the ABC transporter after synthesis, a phenomenon we previously determined to be caused by an excessive positive charge in certain regions of their amino acid sequence. If such an excessive charge is removed, the secretion of any protein through ABC transporters becomes possible.In this study, we introduce 'linear charge density' as the criteria for possibility of protein secretion through ABC transporters and confirm that this criterion can be applied to various non-secretable proteins, such as SARS-CoV-2 spike proteins, botulinum toxin light chain, and human growth factors. Additionally, we develop a new algorithm, PySupercharge, that enables the secretion of proteins containing regions with high linear charge density. It selectively converts positively charged amino acids into negatively charged or neutral amino acids after linear charge density analysis to enable protein secretion through ABC transporters.RESULTSIn this study, we introduce 'linear charge density' as the criteria for possibility of protein secretion through ABC transporters and confirm that this criterion can be applied to various non-secretable proteins, such as SARS-CoV-2 spike proteins, botulinum toxin light chain, and human growth factors. Additionally, we develop a new algorithm, PySupercharge, that enables the secretion of proteins containing regions with high linear charge density. It selectively converts positively charged amino acids into negatively charged or neutral amino acids after linear charge density analysis to enable protein secretion through ABC transporters.PySupercharge, which also minimizes functional/structural stability loss of the pre-mutation proteins through the use of sequence conservation data, is currently being operated on an accessible web server. We verified the efficacy of PySupercharge-driven protein supercharging by secreting various previously non-secretable proteins commonly used in research, and so suggest this tool for use in future research requiring effective protein production.CONCLUSIONSPySupercharge, which also minimizes functional/structural stability loss of the pre-mutation proteins through the use of sequence conservation data, is currently being operated on an accessible web server. We verified the efficacy of PySupercharge-driven protein supercharging by secreting various previously non-secretable proteins commonly used in research, and so suggest this tool for use in future research requiring effective protein production. The process of producing proteins in bacterial systems and secreting them through ATP-binding cassette (ABC) transporters is an area that has been actively researched and used due to its high protein production capacity and efficiency. However, some proteins are unable to pass through the ABC transporter after synthesis, a phenomenon we previously determined to be caused by an excessive positive charge in certain regions of their amino acid sequence. If such an excessive charge is removed, the secretion of any protein through ABC transporters becomes possible. In this study, we introduce 'linear charge density' as the criteria for possibility of protein secretion through ABC transporters and confirm that this criterion can be applied to various non-secretable proteins, such as SARS-CoV-2 spike proteins, botulinum toxin light chain, and human growth factors. Additionally, we develop a new algorithm, PySupercharge, that enables the secretion of proteins containing regions with high linear charge density. It selectively converts positively charged amino acids into negatively charged or neutral amino acids after linear charge density analysis to enable protein secretion through ABC transporters. PySupercharge, which also minimizes functional/structural stability loss of the pre-mutation proteins through the use of sequence conservation data, is currently being operated on an accessible web server. We verified the efficacy of PySupercharge-driven protein supercharging by secreting various previously non-secretable proteins commonly used in research, and so suggest this tool for use in future research requiring effective protein production. The process of producing proteins in bacterial systems and secreting them through ATP-binding cassette (ABC) transporters is an area that has been actively researched and used due to its high protein production capacity and efficiency. However, some proteins are unable to pass through the ABC transporter after synthesis, a phenomenon we previously determined to be caused by an excessive positive charge in certain regions of their amino acid sequence. If such an excessive charge is removed, the secretion of any protein through ABC transporters becomes possible. In this study, we introduce 'linear charge density' as the criteria for possibility of protein secretion through ABC transporters and confirm that this criterion can be applied to various non-secretable proteins, such as SARS-CoV-2 spike proteins, botulinum toxin light chain, and human growth factors. Additionally, we develop a new algorithm, PySupercharge, that enables the secretion of proteins containing regions with high linear charge density. It selectively converts positively charged amino acids into negatively charged or neutral amino acids after linear charge density analysis to enable protein secretion through ABC transporters. PySupercharge, which also minimizes functional/structural stability loss of the pre-mutation proteins through the use of sequence conservation data, is currently being operated on an accessible web server. We verified the efficacy of PySupercharge-driven protein supercharging by secreting various previously non-secretable proteins commonly used in research, and so suggest this tool for use in future research requiring effective protein production. Background The process of producing proteins in bacterial systems and secreting them through ATP-binding cassette (ABC) transporters is an area that has been actively researched and used due to its high protein production capacity and efficiency. However, some proteins are unable to pass through the ABC transporter after synthesis, a phenomenon we previously determined to be caused by an excessive positive charge in certain regions of their amino acid sequence. If such an excessive charge is removed, the secretion of any protein through ABC transporters becomes possible. Results In this study, we introduce 'linear charge density' as the criteria for possibility of protein secretion through ABC transporters and confirm that this criterion can be applied to various non-secretable proteins, such as SARS-CoV-2 spike proteins, botulinum toxin light chain, and human growth factors. Additionally, we develop a new algorithm, PySupercharge, that enables the secretion of proteins containing regions with high linear charge density. It selectively converts positively charged amino acids into negatively charged or neutral amino acids after linear charge density analysis to enable protein secretion through ABC transporters. Conclusions PySupercharge, which also minimizes functional/structural stability loss of the pre-mutation proteins through the use of sequence conservation data, is currently being operated on an accessible web server. We verified the efficacy of PySupercharge-driven protein supercharging by secreting various previously non-secretable proteins commonly used in research, and so suggest this tool for use in future research requiring effective protein production. Keywords: Python Algorithm, Supercharging, Protein production, Secretion, ABC Transporter Background The process of producing proteins in bacterial systems and secreting them through ATP-binding cassette (ABC) transporters is an area that has been actively researched and used due to its high protein production capacity and efficiency. However, some proteins are unable to pass through the ABC transporter after synthesis, a phenomenon we previously determined to be caused by an excessive positive charge in certain regions of their amino acid sequence. If such an excessive charge is removed, the secretion of any protein through ABC transporters becomes possible. Results In this study, we introduce ‘linear charge density’ as the criteria for possibility of protein secretion through ABC transporters and confirm that this criterion can be applied to various non-secretable proteins, such as SARS-CoV-2 spike proteins, botulinum toxin light chain, and human growth factors. Additionally, we develop a new algorithm, PySupercharge, that enables the secretion of proteins containing regions with high linear charge density. It selectively converts positively charged amino acids into negatively charged or neutral amino acids after linear charge density analysis to enable protein secretion through ABC transporters. Conclusions PySupercharge, which also minimizes functional/structural stability loss of the pre-mutation proteins through the use of sequence conservation data, is currently being operated on an accessible web server. We verified the efficacy of PySupercharge-driven protein supercharging by secreting various previously non-secretable proteins commonly used in research, and so suggest this tool for use in future research requiring effective protein production. Abstract Background The process of producing proteins in bacterial systems and secreting them through ATP-binding cassette (ABC) transporters is an area that has been actively researched and used due to its high protein production capacity and efficiency. However, some proteins are unable to pass through the ABC transporter after synthesis, a phenomenon we previously determined to be caused by an excessive positive charge in certain regions of their amino acid sequence. If such an excessive charge is removed, the secretion of any protein through ABC transporters becomes possible. Results In this study, we introduce ‘linear charge density’ as the criteria for possibility of protein secretion through ABC transporters and confirm that this criterion can be applied to various non-secretable proteins, such as SARS-CoV-2 spike proteins, botulinum toxin light chain, and human growth factors. Additionally, we develop a new algorithm, PySupercharge, that enables the secretion of proteins containing regions with high linear charge density. It selectively converts positively charged amino acids into negatively charged or neutral amino acids after linear charge density analysis to enable protein secretion through ABC transporters. Conclusions PySupercharge, which also minimizes functional/structural stability loss of the pre-mutation proteins through the use of sequence conservation data, is currently being operated on an accessible web server. We verified the efficacy of PySupercharge-driven protein supercharging by secreting various previously non-secretable proteins commonly used in research, and so suggest this tool for use in future research requiring effective protein production. |
| ArticleNumber | 115 |
| Audience | Academic |
| Author | Kim, Yerin Byun, Hyunjong Ahn, Jung Hoon Kim, Danny Hieu, Nguyen-Mihn |
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| References | R Chen (2342_CR4) 2012; 30 N Ihling (2342_CR10) 2020; 36 R Freudl (2342_CR8) 2018; 17 KM Slade (2342_CR7) 2009; 48 D Novo (2342_CR26) 1999; 35 M Son (2342_CR19) 2012; 78 BS Der (2342_CR28) 2013; 8 CW Chung (2342_CR22) 2009; 8 H Byun (2342_CR1) 2017 Y Park (2342_CR17) 2012; 11 D Dressler (2342_CR33) 2005; 53 GRM Kleiner-Grote (2342_CR9) 2018; 18 J Ryu (2342_CR18) 2015; 81 G Celniker (2342_CR31) 2013; 53 2342_CR25 2342_CR24 S Letoffe (2342_CR13) 1996; 15 RE Dalbey (2342_CR20) 2012; 36 2342_CR32 2342_CR11 J Young (2342_CR14) 1999; 1461 HP Sorensen (2342_CR5) 2005; 4 MS Lawrence (2342_CR27) 2007; 129 PJ Bakkes (2342_CR23) 2010; 285 JH Ahn (2342_CR15) 1999; 181 GL Rosano (2342_CR6) 2014; 5 S Thomas (2342_CR12) 2014; 1843 LA Burdette (2342_CR21) 2018; 17 H Ashkenazy (2342_CR30) 2010; 38 DB Thompson (2342_CR34) 2012; 503 H Ashkenazy (2342_CR29) 2016; 44 B-U Fabia (2342_CR2) 2021; 9 IB Holland (2342_CR3) 2005; 22 2342_CR16 |
| References_xml | – ident: 2342_CR11 doi: 10.1128/microbiolspec.VMBF-0012-2015 – ident: 2342_CR32 doi: 10.1038/s41586-021-03819-2 – volume: 17 start-page: 196 issue: 1 year: 2018 ident: 2342_CR21 publication-title: Microb Cell Fact doi: 10.1186/s12934-018-1041-5 – volume: 38 start-page: W529 issue: suppl2 year: 2010 ident: 2342_CR30 publication-title: Nucleic Acids Res doi: 10.1093/nar/gkq399 – volume: 1461 start-page: 177 issue: 2 year: 1999 ident: 2342_CR14 publication-title: Biochim et Biophys Acta (BBA)-Biomembranes doi: 10.1016/S0005-2736(99)00158-3 – volume: 53 start-page: 199 issue: 3–4 year: 2013 ident: 2342_CR31 publication-title: Isr J Chem doi: 10.1002/ijch.201200096 – volume: 9 start-page: 679 issue: 6 year: 2021 ident: 2342_CR2 publication-title: Biomedicines doi: 10.3390/biomedicines9060679 – volume: 129 start-page: 10110 issue: 33 year: 2007 ident: 2342_CR27 publication-title: J Am Chem Soc doi: 10.1021/ja071641y – ident: 2342_CR16 doi: 10.3390/microorganisms8020239 – volume: 81 start-page: 1744 issue: 5 year: 2015 ident: 2342_CR18 publication-title: Appl Environ Microbiol doi: 10.1128/AEM.03514-14 – volume: 36 start-page: e2911 issue: 2 year: 2020 ident: 2342_CR10 publication-title: Biotechnol Prog doi: 10.1002/btpr.2911 – volume: 15 start-page: 5804 issue: 21 year: 1996 ident: 2342_CR13 publication-title: EMBO J doi: 10.1002/j.1460-2075.1996.tb00967.x – volume: 8 start-page: e64363 issue: 5 year: 2013 ident: 2342_CR28 publication-title: PLoS ONE doi: 10.1371/journal.pone.0064363 – ident: 2342_CR24 doi: 10.1128/AEM.01163-17 – volume: 30 start-page: 1102 issue: 5 year: 2012 ident: 2342_CR4 publication-title: Biotechnol Adv doi: 10.1016/j.biotechadv.2011.09.013 – volume: 48 start-page: 5083 issue: 23 year: 2009 ident: 2342_CR7 publication-title: Biochemistry doi: 10.1021/bi9004107 – volume: 285 start-page: 40573 issue: 52 year: 2010 ident: 2342_CR23 publication-title: J Biol Chem doi: 10.1074/jbc.M110.173658 – volume: 78 start-page: 8454 issue: 23 year: 2012 ident: 2342_CR19 publication-title: Appl Environ Microbiol doi: 10.1128/AEM.02476-12 – volume: 44 start-page: W344 issue: W1 year: 2016 ident: 2342_CR29 publication-title: Nucleic Acids Res doi: 10.1093/nar/gkw408 – volume: 35 start-page: 55 issue: 1 year: 1999 ident: 2342_CR26 publication-title: Cytometry doi: 10.1002/(sici)1097-0320(19990101)35:1<55::aid-cyto8>3.0.co;2-2 – volume: 11 start-page: 60 year: 2012 ident: 2342_CR17 publication-title: Microb Cell Fact doi: 10.1186/1475-2859-11-601475-2859-11-60 – volume: 181 start-page: 1847 issue: 6 year: 1999 ident: 2342_CR15 publication-title: J Bacteriol doi: 10.1128/JB.181.6.1847-1852.1999 – volume: 1843 start-page: 1629 issue: 8 year: 2014 ident: 2342_CR12 publication-title: Biochim Biophys Acta doi: 10.1016/j.bbamcr.2013.09 – volume: 8 start-page: 11 issue: 1 year: 2009 ident: 2342_CR22 publication-title: Microb Cell Fact doi: 10.1186/1475-2859-8-11 – volume: 17 start-page: 52 issue: 1 year: 2018 ident: 2342_CR8 publication-title: Microb Cell Fact doi: 10.1186/s12934-018-0901-3 – volume: 5 start-page: 172 year: 2014 ident: 2342_CR6 publication-title: Front Microbiol doi: 10.3389/fmicb.2014.00172 – volume: 22 start-page: 29 issue: 1–2 year: 2005 ident: 2342_CR3 publication-title: Mol Membr Biol doi: 10.1080/09687860500042013 – volume: 18 start-page: 532 issue: 8 year: 2018 ident: 2342_CR9 publication-title: Eng Life Sci doi: 10.1002/elsc.201700200 – year: 2017 ident: 2342_CR1 publication-title: J Biol Chem doi: 10.1074/jbc.M117.786749 – volume: 36 start-page: 1023 issue: 6 year: 2012 ident: 2342_CR20 publication-title: FEMS Microbiol Rev doi: 10.1111/j.1574-6976.2012.00327.x – volume: 53 start-page: 3 issue: 1 year: 2005 ident: 2342_CR33 publication-title: Eur Neurol doi: 10.1159/000083259 – ident: 2342_CR25 doi: 10.3390/ijms23126700 – volume: 4 start-page: 1 issue: 1 year: 2005 ident: 2342_CR5 publication-title: Microb Cell Fact doi: 10.1186/1475-2859-4-1 – volume: 503 start-page: 293 year: 2012 ident: 2342_CR34 publication-title: Methods Enzymol doi: 10.1016/B978-0-12-396962-0.00012-4 |
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The process of producing proteins in bacterial systems and secreting them through ATP-binding cassette (ABC) transporters is an area that has been... The process of producing proteins in bacterial systems and secreting them through ATP-binding cassette (ABC) transporters is an area that has been actively... Background The process of producing proteins in bacterial systems and secreting them through ATP-binding cassette (ABC) transporters is an area that has been... Abstract Background The process of producing proteins in bacterial systems and secreting them through ATP-binding cassette (ABC) transporters is an area that... |
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| StartPage | 115 |
| SubjectTerms | ABC Transporter Algorithms Amino acids Analysis Applied Microbiology Biotechnology Chemistry Chemistry and Materials Science Enzymology Genetic Engineering Health aspects Information management Methodology Microbial Genetics and Genomics Microbiology Protein production Proteins Python (Programming language) Python Algorithm Secretion Supercharging |
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| Title | PySupercharge: a python algorithm for enabling ABC transporter bacterial secretion of all proteins through amino acid mutation |
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