Methods, models, systems, and apparatus for identifying target sequences for cas enzymes for crispr-cas systems for target sequences and conveying results thereof

Patent metadata

Field	Value
Publication number	US-2021366572-A1
Application number	US-202117182817-A
Country	US
Kind code	A1
Filing date	Feb 23, 2021
Priority date	Dec 12, 2012
Publication date	Nov 25, 2021
Grant date	—

Abstract

Disclosed are thermodynamic and multiplication methods concerning CRISPR-Cas systems, and apparatus therefor.

First claim

1 . A method of identifying one or more unique target sequences in a genome of a eukaryotic cell, whereby the target sequence is susceptible to being recognized by a CRISPR-Cas system, wherein the method comprises:
a) determining average cutting frequency of a particular mismatch position for a particular Cas nuclease from a training data set as to that Cas nuclease, wherein the mismatch is between a CRISPR-Cas system guide and a target sequence, and b) determining average cutting frequency of a particular mismatch type for the particular Cas nuclease from the training data set, to thereby obtain a ranking and identify one or more unique target sequences based on the ranking.

2 . The method of claim 1 , comprising
c) multiplying the average cutting frequency of a particular mismatch position by the average cutting frequency of a particular mismatch type to obtain a first product, d) repeating steps a) to c) to obtain second and optionally further products for any further particular mismatch position(s) and mismatch type(s) and multiplying those second and optionally further products by the first product, for an ultimate product, and omitting this step if there is no mismatch at any position or if there is only one particular mismatch at one particular position, and e) multiplying the ultimate product by the result of dividing the minimum distance between consecutive mismatches by the distance, in bp, between the first and last base of the target sequence and omitting this step if there is no mismatch at any position or if there is only one particular mismatch at one particular position, to thereby obtain the ranking.

3 . The method of claim 1 , comprising creating the training data set as to a particular Cas nuclease before performing step (a).

4 . The method of claim 2 , wherein the distance, in bp, between the first and last base of the target sequence is 18.

5 . The method of claim 1 , wherein the average cutting frequency p est is determined from p est ∝e −βZ est , where β is a positive constant of proportionality and Z est is the effective free-energy determined using = where G is the local free energy and a are position dependent weights determined using the training set.

6 . The method of claim 1 , further comprising producing a composition comprising (a) the Cas nuclease or a nucleic acid encoding therefor and (b) a CRISPR-Cas system guide or a nucleic acid encoding therefor, wherein the CRISPR-Cas system guide is capable of hybridizing to the unique target sequence and directing the Cas nuclease to the unique target sequence in the genome of the eukaryotic cell.

7 . The method of claim 6 , wherein the Cas nuclease is Cas9.

8 . The method of claim 6 , wherein the CRISPR-Cas system guide is a chimeric guide RNA.

9 . The method of claim 6 , wherein the target sequence is a target DNA sequence.

10 . The method of claim 6 , further comprising delivering the composition into the eukaryotic cell, wherein a CRISPR complex formed by the Cas nuclease and CRISPR-Cas system guide cleaves the target sequence within the eukaryotic cell.

11 . The method of claim 1 , wherein the Cas nuclease is Cas9.

12 . The method of claim 1 , wherein the CRISPR-Cas system guide is a chimeric guide RNA.

13 . The method of claim 1 , wherein the target sequence is a target DNA sequence.

14 . The method of claim 1 , wherein the target sequence is adjacent to a protospacer adjacent motif (PAM) in the genome of the eukaryotic cell.

15 . The method of claim 14 , wherein the PAM comprises NGG, NNAGAAW, or NNGRR.

Assignees

• Broad Inst Inc
• Massachusetts Inst Technology
• Harvard College

Inventors

• Zhang Feng
• Li Yinqing
• Scott David Arthur
• Weinstein Joshua Asher
• Hsu Patrick

Classifications

• G16B20/00

  ICT specially adapted for functional genomics or proteomics, e.g. genotype-phenotype associations · CPC title

• G16B20/30 Primary

  Detection of binding sites or motifs · CPC title

• C12N15/1089

  Design, preparation, screening or analysis of libraries using computer algorithms · CPC title

• C12N15/113

  Non-coding nucleic acids modulating the expression of genes, e.g. antisense oligonucleotides; {Antisense DNA or RNA; Triplex- forming oligonucleotides; Catalytic nucleic acids, e.g. ribozymes; Nucleic acids used in co-suppression or gene silencing (when used in plants C12N15/8218)} · CPC title

• C12N9/22

  Ribonucleases {[RNase]; Deoxyribonucleases [DNase]} · CPC title