DK2278509T3 - 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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DK2278509T3
DK2278509T3 DK10181000.0T DK10181000T DK2278509T3 DK 2278509 T3 DK2278509 T3 DK 2278509T3 DK 10181000 T DK10181000 T DK 10181000T DK 2278509 T3 DK2278509 T3 DK 2278509T3
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population
elements
selecting
identifying
sequence
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DK10181000.0T
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Claes Gustafsson
Sridar Govindarajan
Richard John Fox
Ajoy Roy
Anthony Cox
Phil Patten
Linda A Castle
Daniel L Siehl
Teddy Chen
Robin Emig
Jeremy Minshull
S Christopher Davis
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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  • Engineering & Computer Science (AREA)
  • Health & Medical Sciences (AREA)
  • Life Sciences & Earth Sciences (AREA)
  • Physics & Mathematics (AREA)
  • Bioinformatics & Cheminformatics (AREA)
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  • Biotechnology (AREA)
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  • Proteomics, Peptides & Aminoacids (AREA)
  • Genetics & Genomics (AREA)
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  • Peptides Or Proteins (AREA)
  • Investigating Or Analysing Biological Materials (AREA)
  • Information Retrieval, Db Structures And Fs Structures Therefor (AREA)
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Claims (16)

1. Fremgangsmåde til identificering af elementer afen population af polypep-tid-sekvensvarianter, der er mest egnede til kunstig evolution, hvilken fremgangsmåde omfatter: (a) at udvælge eller screene elementerne af en population af polypeptid-sekvensvarianter for to eller flere ønskede strukturelle og/eller funktionelle egenskaber med henblik på at udarbejde et datasæt; (b) at identificere en Pareto-front i datasættet; (c) at udvælge et eller flere elementer langs med Pareto-fronten, hvorved der identificeres de elementer af populationen af polypeptid-sekvensvarianter, der er mest egnede til kunstig evolution; og (d) at syntetisere elementerne udvalgt i trin (c).
2. Fremgangsmåde til identificering af elementer af en population af polynu-kleotid-sekvensvarianter, der er mest egnede til kunstig evolution, hvilken fremgangsmåde omfatter: (a) at udvælge eller screene elementerne af en population af polynukleotid-sekvensvarianter for to eller flere strukturelle og/eller funktionelle egenskaber med henblik på at udarbejde et datasæt; (b) at identificere en Pareto-front i datasættet; (c) at udvælge et eller flere elementer langs med Pareto-fronten, hvorved der identificeres de elementer af populationen af polynukleotid-sekvensvarianter, der er mest egnede til kunstig evolution; og (d) at syntetisere elementerne udvalgt i trin (c).
3. Fremgangsmåde ifølge krav 1 eller 2, hvor trin (c) omfatter: (i) at kalkulere en vægtet sum af de to eller flere ønskede strukturelle og/eller funktionelle egenskaber for mindst nogle af elementerne langs med Pareto-fronten; og (ii) at udvælge mindst et element omfattende en højere vægtet sum end andre elementer langs med Pareto-fronten.
4. Fremgangsmåde ifølge krav 1 eller 2, hvor trin (c) omfatter: (i) at rangordne det ene eller de flere elementer langs med Pareto-fronten efter relativ isolation i sekvensrummet; og (ii) at udvælge mindst et element, som rangerer højere end andre elementer langs med Pareto-fronten.
5. Fremgangsmåde ifølge krav 1, hvilken fremgangsmåde omfatter: (a) at fremstille en population af polypeptid-sekvensvarianter; (b) at udvælge eller screene elementerne af populationen af polypeptid-sekvensvarianter for to eller flere strukturelle og/eller funktionelle egenskaber med henblik på at udarbejde et datasæt; (c) at identificere en Pareto-front i datasættet; (d) at udvælge et eller flere elementer langs med Pareto-fronten, hvorved der identificeres de elementer af populationen af polypeptid-sekvensvarianter, der er mest egnede til kunstig evolution; og (e) at syntetisere elementerne udvalgt i trin (d).
6. Fremgangsmåde ifølge krav 2, hvilken fremgangsmåde omfatter: (a) at fremstille en population af polynukleotid-sekvensvarianter; (b) at udvælge eller screene elementerne af populationen af polynukleotid-sekvensvarianter for to eller flere strukturelle og/eller funktionelle egenskaber med henblik på at udarbejde et datasæt; (c) at identificere en Pareto-front i datasættet; (d) at udvælge et eller flere elementer langs med Pareto-fronten, hvorved der identificeres de elementer af populationen af polynukleotid-sekvensvarianter, der er mest egnede til kunstig evolution; og (e) at syntetisere elementerne udvalgt i trin (d).
7. Fremgangsmåde ifølge krav 5 eller 6, hvor trin (d) omfatter: (i) at kalkulere en vægtet sum af de to eller flere ønskede strukturelle og/eller funktionelle egenskaber for mindst nogle af elementerne langs med Pareto- fronten; og (ii) at udvælge mindst et element omfattende en højere vægtet sum end andre elementer langs med Pareto-fronten.
8. Fremgangsmåde ifølge krav 5 eller 6, hvor trin (d) omfatter: (i) at rangordne det ene eller de flere elementer langs med Pareto-fronten efter relativ isolation i sekvensrummet; og (ii) at udvælge mindst et element, som rangerer højere end andre elementer langs med Pareto-fronten.
9. Fremgangsmåde ifølge krav 4 eller 8, hvor rangordningen omfatter at opdele Pareto-fronten i hyperboxe og fortrinsvis at udvælge elementer fra mindre befolkede hyperboxe.
10. Fremgangsmåde ifølge et af kravene 1, 2, 5 eller 6, hvor de to eller de flere strukturelle og/eller funktionelle egenskaber omfatter begrænsninger, værdier, der detaljerer afstanden fra at opnå begrænsninger, et samlet antal af opfyldte begrænsninger, et relativt antal af opfyldte begrænsninger, og/eller konkurrerende eller ikke-konkurrerende strukturelle og/eller funktionelle egenskaber af populationen af biopolymere sekvensvarianter.
11. Fremgangsmåde ifølge et af kravene 1, 2, 5 eller 6, hvor de to eller de flere strukturelle og/eller funktionelle egenskaber er ortogonale i forhold til hinanden.
12. Fremgangsmåde ifølge et af kravene 1,2, 5 eller 6, hvor udvælgelsen af det ene eller de flere elementer langs med Pareto-fronten omfatter at anvende en niching-teknik.
13. Computerprogramprodukt til identificering af elementer af en population af polypeptid-sekvensvarianter, der er mest egnede til kunstig evolution, omfattende et computerlæsbart medium, som har logiske instruktioner for: (a) at udvælge eller screene elementerne af en population af polypeptid-sekvensvarianter for to eller flere ønskede strukturelle og/eller funktionelle egenskaber med henblik på at udarbejde et datasæt; (b) at identificere en Pareto-front i datasættet; og (c) at udvælge et eller flere elementer langs med Pareto-fronten, hvorved der identificeres de elementer af populationen af polypeptid-sekvensvarianter, der er mest egnede til kunstig evolution.
14. Computerprogramprodukt til identificering af elementer af en population af polypeptid-sekvensvarianter, der er mest egnede til kunstig evolution, omfattende et computerlæsbart medium, som har logiske instruktioner for: (a) at udvælge eller screene elementerne af en population af polynukleotid-sekvensvarianter for to eller flere strukturelle og/eller funktionelle egenskaber med henblik på at udarbejde et datasæt; (b) at identificere en Pareto-front i datasættet; og (c) at udvælge et eller flere elementer langs med Pareto-fronten, hvorved der identificeres de elementer af populationen af polynukleotid-sekvensvarianter, der er mest egnede til kunstig evolution.
15. Computersystem til identificering af elementer af en population af po-lypeptid-sekvensvarianter, der er mest egnede til kunstig evolution, omfattende: (i) en computer med en database, som er i stand til at lagre datasættet ifølge krav 1; og (ii) systemsoftware, som indeholder logiske instruktioner for: (a) at udvælge eller screene elementerne af en population af polypeptid-sekvensvarianter for to eller flere ønskede strukturelle og/eller funktionelle egenskaber med henblik på at udarbejde et datasæt; (b) at identificere en Pareto-front i datasættet; og (c) at udvælge et eller flere elementer langs med Pareto-fronten, hvorved der identificeres de elementer af populationen af polypeptid-sekvensvarianter, der er mest egnede til kunstig evolution.
16. Computersystem til identificering af elementer afen population af polynukleotid-sekvensvarianter, der er mest egnede til kunstig evolution, omfattende: (i) en computer med en database, som er i stand til at lagre datasættet ifølge krav 2; og (ii) systemsoftware, som indeholder logiske instruktioner for: (a) at udvælge eller screene elementerne af en population af polynukleotid-sekvensvarianter for to eller flere strukturelle og/eller funktionelle egenskaber med henblik på at udarbejde et datasæt; (b) at identificere en Pareto-front i datasættet; og (c) at udvælge et eller flere elementer langs med Pareto-fronten, hvorved der identificeres de elementer af populationen af polynukleotid-sekvensvarianter, der er mest egnede til kunstig evolution.
DK10181000.0T 2002-03-01 2003-03-03 Fremgangsmåder, systemer og software til identificering af funktionelle biomolekyler DK2278509T3 (da)

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JP (2) JP5319865B2 (da)
AU (1) AU2003216507A1 (da)
DK (4) DK2278509T3 (da)
ES (1) ES2564570T3 (da)
HU (1) HUE028524T2 (da)
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