DK1493027T3 - Fremgangsmåder, systemer og software til identificering af funktionelle biomolekyler - Google Patents

Fremgangsmåder, systemer og software til identificering af funktionelle biomolekyler Download PDF

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DK1493027T3
DK1493027T3 DK03743748.0T DK03743748T DK1493027T3 DK 1493027 T3 DK1493027 T3 DK 1493027T3 DK 03743748 T DK03743748 T DK 03743748T DK 1493027 T3 DK1493027 T3 DK 1493027T3
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sequence
activity
sequences
amino acid
library
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Sridar Govindarajan
Robin Emig
Richard John Fox
Ajoy Roy
Jeremy Minshull
Anthony Cox
Phil Patten
Teddy Chen
Claes Gustafsson
S Christopher Davis
Linda A Castle
Daniel L Siehl
Rebecca Lynne Gorton
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Codexis Mayflower Holdings Llc
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    • G—PHYSICS
    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16B—BIOINFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR GENETIC OR PROTEIN-RELATED DATA PROCESSING IN COMPUTATIONAL MOLECULAR BIOLOGY
    • G16B20/00—ICT specially adapted for functional genomics or proteomics, e.g. genotype-phenotype associations
    • G—PHYSICS
    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16B—BIOINFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR GENETIC OR PROTEIN-RELATED DATA PROCESSING IN COMPUTATIONAL MOLECULAR BIOLOGY
    • G16B20/00—ICT specially adapted for functional genomics or proteomics, e.g. genotype-phenotype associations
    • G16B20/30—Detection of binding sites or motifs
    • G—PHYSICS
    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16B—BIOINFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR GENETIC OR PROTEIN-RELATED DATA PROCESSING IN COMPUTATIONAL MOLECULAR BIOLOGY
    • G16B20/00—ICT specially adapted for functional genomics or proteomics, e.g. genotype-phenotype associations
    • G16B20/50—Mutagenesis
    • G—PHYSICS
    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16B—BIOINFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR GENETIC OR PROTEIN-RELATED DATA PROCESSING IN COMPUTATIONAL MOLECULAR BIOLOGY
    • G16B35/00—ICT specially adapted for in silico combinatorial libraries of nucleic acids, proteins or peptides
    • G—PHYSICS
    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16B—BIOINFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR GENETIC OR PROTEIN-RELATED DATA PROCESSING IN COMPUTATIONAL MOLECULAR BIOLOGY
    • G16B35/00—ICT specially adapted for in silico combinatorial libraries of nucleic acids, proteins or peptides
    • G16B35/20—Screening of libraries
    • G—PHYSICS
    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16B—BIOINFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR GENETIC OR PROTEIN-RELATED DATA PROCESSING IN COMPUTATIONAL MOLECULAR BIOLOGY
    • G16B40/00—ICT specially adapted for biostatistics; ICT specially adapted for bioinformatics-related machine learning or data mining, e.g. knowledge discovery or pattern finding
    • G16B40/20—Supervised data analysis
    • G—PHYSICS
    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16C—COMPUTATIONAL CHEMISTRY; CHEMOINFORMATICS; COMPUTATIONAL MATERIALS SCIENCE
    • G16C20/00—Chemoinformatics, i.e. ICT specially adapted for the handling of physicochemical or structural data of chemical particles, elements, compounds or mixtures
    • G16C20/60—In silico combinatorial chemistry
    • G—PHYSICS
    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16B—BIOINFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR GENETIC OR PROTEIN-RELATED DATA PROCESSING IN COMPUTATIONAL MOLECULAR BIOLOGY
    • G16B40/00—ICT specially adapted for biostatistics; ICT specially adapted for bioinformatics-related machine learning or data mining, e.g. knowledge discovery or pattern finding

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  • Proteomics, Peptides & Aminoacids (AREA)
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  • Information Retrieval, Db Structures And Fs Structures Therefor (AREA)
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Claims (28)

1. Fremgangsmåde til generering af et hidtil ukendt proteinvariant-bibliotek, hvilken fremgangsmåde omfatter: (a) at modtage data, der kendetegner et træningssæt af et proteinvariant-bibliotek, hvor dataene tilvejebringer aktivitets- og sekvensinformation for hver proteinvariant i træningssættet; (b) ud fra dataene at udvikle en sekvensaktivitetsmodel, der er en regressionsmodel, der behandler aktiviteten som en afhængig variabel og se-kvens/rest-værdierne som uafhængige variabler, og som kan skelne rester, der har en signifikant indvirkning på aktiviteten, fra dem, der ikke har, og forudsiger aktiviteten som en funktion af aminosyreresttypen og den tilsvarende position i sekvensen; (c) at anvende sekvensaktivitetsmodellen til at identificere en eller flere ami-nosyrerester i specifikke positioner, der skal varieres i en eller flere proteinvarianter af et hidtil ukendt proteinvariant-bibliotek for at påvirke aktiviteten, hvor specifikke rester, der forudsiges at have den største fordelagtige indvirkning på aktiviteten, konserveres, og et bestemt antal af andre rester, der forudsiges at have en mindre fordelagtig indvirkning på aktiviteten, vælges til variation; (d) at identificere en eller flere sekvenser, der skal anvendes til at generere et hidtil ukendt proteinvariant-bibliotek med den specificerede restvariation; (e) at vælge et eller flere elementer af det hidtil ukendte proteinvariant-bibliotek til produktion; (f) at producere de valgte elementer af det hidtil ukendte proteinvariant-bibliotek; og (g) at screene de producerede elementer af det hidtil ukendte proteinvariant-bibliotek til aktiviteten for at identificere et eller flere elementer af det hidtil ukendte proteinvariant-bibliotek med bedre aktivitet, end der blev observeret i træningssættet.
2. Fremgangsmåde ifølge krav 1, hvor proteinvarianter i træningssættet af et proteinvariant-bibliotek har systematisk varierede sekvenser.
3. Fremgangsmåde ifølge krav 2, hvor træningssættet af et proteinvariant-bibliotek omfatter naturligt forekommende proteiner eller proteiner, der er afledt deraf.
4. Fremgangsmåde ifølge krav 3, hvor de naturligt forekommende proteiner omfatter proteiner, der kodes af elementer af en enkelt genfamilie.
5. Fremgangsmåde ifølge krav 2, hvor træningssættet af et proteinvariant-bibliotek omfatter proteiner, der opnås ved anvendelse af en rekombinationsbaseret mekanisme til diversitetsgenerering.
6. Fremgangsmåde ifølge krav 2, hvor de systematisk varierede sekvenser designes a priori ved anvendelse af DOE (design of experiment)-fremgangsmåder til at definere sekvenserne i træningssættet.
7. Fremgangsmåde ifølge krav 2, hvor aktiviteten ikke er proteinstabilitet.
8. Fremgangsmåde ifølge et af de foregående krav, hvor sekvensaktivitetsmodellen er en delvis-mindste-kvadraters-model.
9. Fremgangsmåde ifølge krav 2, hvor anvendelsen af sekvensaktivitetsmodellen til at identificere en eller flere aminosyrerester endvidere omfatter at identificere sekvenser til anvendelse i rekombinationsbaseret mekanisme til diversitetsgenerering, hvor sekvenserne omfatter variationer i den ene eller flere aminosyrerester, der blev identificeret i (c).
10. Fremgangsmåde ifølge krav 2, hvor anvendelse af sekvensaktivitetsmodellen omfatter at identificere en sekvens, der af modellen forudsiges at have en højeste værdi af den ønskede aktivitet.
11. Fremgangsmåde ifølge krav 10, hvor anvendelsen af modellen endvidere omfatter at vælge undersekvenser af den bedste sekvens.
12. Fremgangsmåde ifølge krav 2, hvor anvendelse af sekvensaktivitetsmodellen til at identificere en eller flere aminosyrerester omfatter at anvende sekvensaktivitetsmodellen til at rangordne restpositioner efter indvirkning på den ønskede aktivitet.
13. Fremgangsmåde ifølge krav 2, hvor anvendelse af sekvensaktivitetsmodellen til at identificere en eller flere aminosyrerester omfatter at anvende sekvensaktivitetsmodellen til at rangordne resttyper i restpositioner efter indvirkning på den ønskede aktivitet.
14. Fremgangsmåde ifølge krav 2, hvor sekvenserne er oligonukleotidse-kvenser, der koder for variationer af den ene eller flere identificerede aminosyrerester.
15. Fremgangsmåde ifølge krav 14, endvidere omfattende at udføre mutage-nese eller en rekombinationsbaseret mekanisme til diversitetsgenerering ved anvendelse af oligonukleotidsekvenserne til generering af det hidtil ukendte proteinvariant-bibliotek.
16. Fremgangsmåde ifølge krav 15, hvor udførelse af mutagenese eller en rekombinationsbaseret mekanisme til diversitetsgenerering anvendes i en fremgangsmåde til directed evolution.
17. Fremgangsmåde ifølge krav 14, hvor oligonukleotidsekvenserne koder for mindst en del af (i) et naturligt forekommende moderprotein med den højeste aktivitet blandt naturligt forekommende moderproteiner, eller (ii) en sekvens, der af sekvensaktivitetsmodellen forudsiges at have den højeste aktivitet.
18. Fremgangsmåde ifølge krav 2, endvidere omfattende at udvikle en hidtil ukendt sekvensaktivitetsmodel, der anvender aktivitets- og sekvensdata, der kendetegner det hidtil ukendte proteinvariant-bibliotek.
19. Fremgangsmåde ifølge krav 1, omfattende at udtrykke et eller flere af de valgte elementer af det hidtil ukendte proteinvariant-bibliotek.
20. Fremgangsmåde ifølge krav 1, omfattende: (i) at tilvejebringe et ekspressionssystem, ud fra hvilket et valgt element af det hidtil ukendte proteinvariant-bibliotek kan udtrykkes; og (ii) at udtrykke det valgte element af det hidtil ukendte proteinvariant-bibliotek.
21. Fremgangsmåde ifølge krav 2, hvor den ene eller flere aminosyrerester, der blev identificeret i (c), identificeres i en referencesekvens, der forudsiges ved anvendelse af sekvensaktivitetsmodellen eller en referencesekvens, der beskriver et element af proteinvariant-biblioteket.
22. Fremgangsmåde ifølge krav 1, hvor træningssættet af et proteinvariant-bibliotek omfatter proteiner, der blev opnået ved at udføre klassisk eller syntetisk DNA-shuffling på nukleinsyrer, der koder for alle eller en del af et eller flere naturligt forekommende moderproteiner.
23. Fremgangsmåde ifølge krav 1, hvor sekvensaktivitetsmodellen anvendes til at identificere en eller flere aminosyrerester, der skal varieres for at identificere en eller flere sekvenser til anvendelse i en fremgangsmåde til directed evolution.
24. Fremgangsmåde ifølge krav 23, hvor sekvenserne er oligonukleotidse-kvenser, der koder for variationer af den ene eller flere identificerede aminosyrerester.
25. Fremgangsmåde ifølge krav 1, hvor sekvensaktivitetsmodellen anvendes til at rangordne restpositioner eller resttyper i specifikke restpositioner efter indvirkning på den ønskede aktivitet, og fremgangsmåden omfatter at anvende rangfølgen til at identificere en eller flere aminosyrerester i proteiner af proteinvariant-biblioteket, der skal varieres for at påvirke den ønskede aktivitet.
26. Fremgangsmåde ifølge krav 2, som er en fremgangsmåde til generering af et optimeret proteinvariant-bibliotek, omfattende: (a) at modtage data, der kendetegner et træningssæt af et proteinvariant-bibliotek, hvor proteinvarianterne i biblioteket har systematisk varierede se kvenser, og hvor dataene tilvejebringer aktivitets- og sekvensinformation for hver proteinvariant i træningssættet; (b) ud fra dataene at udvikle en sekvensaktivitetsmodel, der forudsiger aktivitet som en funktion af aminosyreresttypen og den tilsvarende position i sekvensen; (c) at anvende sekvensaktivitetsmodellen til at vælge en eller flere aminosy-rerester i specifikke positioner i de systematisk varierede sekvenser, der forudsiges at tilvejebringe ønsket aktivitet; (d) at generere et optimeret proteinvariant-bibliotek, hvor sekvenserne af elementerne af det optimerede proteinvariant-bibliotek hver omfatter den ene eller flere valgte aminosyrerester.
27. Computerindretning, der er indrettet til at udføre fremgangsmåden ifølge et af kravene 1 til 26, hvor indretningen er særligt udformet og/eller konstrueret til at udføre fremgangsmåden ifølge et af kravene 1 til 26.
28. Computerindretning, der er indrettet til at udføre fremgangsmåden ifølge et af kravene 1 til 26, hvor indretningen er en universalcomputer, der selektivt aktiveres eller rekonfigureres af et computerprogram og/eller en datastruktur, der er gemt i computeren til at udføre fremgangsmåden ifølge et af kravene 1 til 26.
DK03743748.0T 2002-03-01 2003-03-03 Fremgangsmåder, systemer og software til identificering af funktionelle biomolekyler DK1493027T3 (da)

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EP (4) EP2390803B1 (da)
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