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1. WO2018104385 - CODON OPTIMIZATION

Publication Number WO/2018/104385
Publication Date 14.06.2018
International Application No. PCT/EP2017/081685
International Filing Date 06.12.2017
IPC
C12N 15/67 2006.01
CCHEMISTRY; METALLURGY
12BIOCHEMISTRY; BEER; SPIRITS; WINE; VINEGAR; MICROBIOLOGY; ENZYMOLOGY; MUTATION OR GENETIC ENGINEERING
NMICROORGANISMS OR ENZYMES; COMPOSITIONS THEREOF; PROPAGATING, PRESERVING, OR MAINTAINING MICROORGANISMS; MUTATION OR GENETIC ENGINEERING; CULTURE MEDIA
15Mutation or genetic engineering; DNA or RNA concerning genetic engineering, vectors, e.g. plasmids, or their isolation, preparation or purification; Use of hosts therefor
09Recombinant DNA-technology
63Introduction of foreign genetic material using vectors; Vectors; Use of hosts therefor; Regulation of expression
67General methods for enhancing the expression
C40B 30/02 2006.01
CCHEMISTRY; METALLURGY
40COMBINATORIAL TECHNOLOGY
BCOMBINATORIAL CHEMISTRY; LIBRARIES, e.g. CHEMICAL LIBRARIES, IN SILICO LIBRARIES
30Methods of screening libraries
02In silico screening
G06F 19/10 2011.01
GPHYSICS
06COMPUTING; CALCULATING OR COUNTING
FELECTRIC DIGITAL DATA PROCESSING
19Digital computing or data processing equipment or methods, specially adapted for specific applications
10Bioinformatics, i.e. methods or systems for genetic or protein-related data processing in computational molecular biology
G06F 19/24 2011.01
GPHYSICS
06COMPUTING; CALCULATING OR COUNTING
FELECTRIC DIGITAL DATA PROCESSING
19Digital computing or data processing equipment or methods, specially adapted for specific applications
10Bioinformatics, i.e. methods or systems for genetic or protein-related data processing in computational molecular biology
24for machine learning, data mining or biostatistics, e.g. pattern finding, knowledge discovery, rule extraction, correlation, clustering or classification
CPC
C12Q 1/6869
CCHEMISTRY; METALLURGY
12BIOCHEMISTRY; BEER; SPIRITS; WINE; VINEGAR; MICROBIOLOGY; ENZYMOLOGY; MUTATION OR GENETIC ENGINEERING
QMEASURING OR TESTING PROCESSES INVOLVING ENZYMES, NUCLEIC ACIDS OR MICROORGANISMS
1Measuring or testing processes involving enzymes, nucleic acids or microorganisms
68involving nucleic acids
6869Methods for sequencing
G16B 25/20
GPHYSICS
16INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
BBIOINFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR GENETIC OR PROTEIN-RELATED DATA PROCESSING IN COMPUTATIONAL MOLECULAR BIOLOGY
25ICT specially adapted for hybridisation; ICT specially adapted for gene or protein expression
20Polymerase chain reaction [PCR]; Primer or probe design; Probe optimisation
G16B 30/00
GPHYSICS
16INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
BBIOINFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR GENETIC OR PROTEIN-RELATED DATA PROCESSING IN COMPUTATIONAL MOLECULAR BIOLOGY
30ICT specially adapted for sequence analysis involving nucleotides or amino acids
G16B 35/00
GPHYSICS
16INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
BBIOINFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR GENETIC OR PROTEIN-RELATED DATA PROCESSING IN COMPUTATIONAL MOLECULAR BIOLOGY
35ICT specially adapted for in silico combinatorial libraries of nucleic acids, proteins or peptides
G16B 40/00
GPHYSICS
16INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
BBIOINFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR GENETIC OR PROTEIN-RELATED DATA PROCESSING IN COMPUTATIONAL MOLECULAR BIOLOGY
40ICT specially adapted for biostatistics; ICT specially adapted for bioinformatics-related machine learning or data mining, e.g. knowledge discovery or pattern finding
G16B 40/20
GPHYSICS
16INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
BBIOINFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR GENETIC OR PROTEIN-RELATED DATA PROCESSING IN COMPUTATIONAL MOLECULAR BIOLOGY
40ICT specially adapted for biostatistics; ICT specially adapted for bioinformatics-related machine learning or data mining, e.g. knowledge discovery or pattern finding
20Supervised data analysis
Applicants
  • MAX-PLANCK-GESELLSCHAFT ZUR FÖRDERUNG DER WISSENSCHAFTEN E.V. [DE]/[DE]
Inventors
  • LIPOWSKY, Reinhard
  • RUDORF, Sophia
  • LÖSSNER, Holger
  • TRÖSEMEIER, Jan-Hendrik
  • KOCH, Ina
  • KAMP, Christel
Agents
  • VOSSIUS & PARTNER (NO 31)
Priority Data
16202752.807.12.2016EP
Publication Language English (EN)
Filing Language English (EN)
Designated States
Title
(EN) CODON OPTIMIZATION
(FR) OPTIMISATION DE CODON
Abstract
(EN)
Method for determining an optimized nucleotide sequence encoding a predetermined amino acid sequence, wherein the nucleotide sequence is optimized for expression in a host cell, and wherein the method comprises the steps of: (a) generating a plurality of candidate nucleotide sequences encoding the predetermined amino acid sequence; (b) obtaining a sequence score based on a scoring function based on a plurality of sequence features that influence protein expression in the host cell using a statistical machine learning algorithm, wherein the plurality of sequence features comprises one or more sequence features selected from the group consisting of protein per time, average elongation rate and accuracy for each of the plurality of candidate nucleotide sequences of step (a); and (c) determining the candidate nucleotide sequence with optimized protein expression in the host cell as the optimized nucleotide sequence.
(FR)
L'invention concerne un procédé de détermination d'une séquence nucléotidique optimisée codant pour une séquence d'acides aminés prédéterminée, la séquence nucléotidique étant optimisée pour l'expression dans une cellule hôte et le procédé comprenant les étapes consistant à : (a) générer une pluralité de séquences nucléotidiques candidates codant pour la séquence d'acides aminés prédéterminée ; (b) obtenir un score de séquence sur la base d'une fonction de notation basée sur une pluralité de caractéristiques de séquence qui influencent l'expression protéinique dans la cellule hôte à l'aide d'un algorithme d'apprentissage machine statistique, la pluralité de caractéristiques de séquence comprenant une ou plusieurs caractéristiques de séquence sélectionnées dans le groupe constitué par la protéine par temps, la vitesse moyenne d'allongement et la précision pour chacune de la pluralité de séquences nucléotidiques candidates de l'étape (a) ; et (c) déterminer la séquence nucléotidique candidate présentant une expression protéinique optimisée dans la cellule hôte en tant que séquence nucléotidique optimisée.
Also published as
EP2017826450
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