Methods for identifying novel gene editing elements

Abstract

Embodiments disclosed herein provide methods for identifying new CRISPR loci and effectors, as well as different CRISPR loci combinations found in various organisms. Class-II CRISPR systems contain single-gene effectors that have been engineered for transformative biological discovery and biomedical applications. Discovery of additional single-gene or multicomponent CRISPR effectors may enhance existing CRISPR applications, such as precision genome engineering. Comprehensive characterization of CRISPR-loci may identify novel functional roles of CRISPR loci enabling new tools for biomedicine and biological discovery. CRISPR loci have enormous feature complexity, but classification of CRISPR loci has been focused on a small fraction of highly abundant features. Increased genome sequencing has enhanced the sampling of this feature complexity.

What is claimed is: 
     
         1 . A computer-implemented method to select a CRISPR-Cas effector for use in treating one or more disease etiologies comprising:
 by at least one computing device:
 classifying a CRISPR locus using an unsupervised machine learning applied to all or a subset of CRISPR locus elements as an initial set of inputs; 
 identifying putative novel effector elements in the CRISPR locus; and 
 screening each identified putative novel effector element for one or more biological functions in a model of a target disease. 
   
     
     
         2 . The method of  claim 1 , wherein the one or more biological functions is nuclease activity or DNA binding activity. 
     
     
         3 . The method of  claim 1 , wherein the one or more diseases or disease etiologies comprises hematopoietic disorders, ocular defects, cardiovascular diseases, hypercholesterolemia, hyperlipidemia, leukemia. 
     
     
         4 . The method of  claim 3 , wherein the hematopoietic disorders comprise Severe Combined Immune Deficiency (SCID). 
     
     
         5 . The method of  claim 3 , wherein the ocular defects comprise macular degeneration (MD), retinitis pigmentosa (RP). 
     
     
         6 . The method of  claim 3 , wherein the cardiovascular diseases comprise high blood pressure, heart attack, heart failure, stroke, transient ischemic attack (TIA). 
     
     
         7 . The method of  claim 1 , wherein the unsupervised machine learning comprises hierarchical clustering. 
     
     
         8 . The method of  claim 7 , wherein hierarchical clustering comprises:
 generating, by the at least one computing device and prior to the classifying step, a preliminary set of CRISPR locus classes by separating a set of known CRISPR loci based, at least in part, on a sequence similarity and/or domain similarity between one or more protein elements of the CRISPR loci;   generating, by the at least one computing device, a distance matrix data structure based, at least in part, on cumulative similarities between constituent proteins in each CRISPR locus class; and   wherein the putative CRISPR loci are classified by applying the hierarchical clustering model to the distance matrix.   
     
     
         9 . The method of  claim 8 , wherein the CRISPR loci are separated based on a domain similarity. 
     
     
         10 . The method of  claim 9 , wherein the domain similarity is determined using a hidden Markov model. 
     
     
         11 . The method of  claim 8 , wherein the cumulative similarities between constitute proteins is determined, at least in part, by calculating a Euclidean distance between each CRISPR locus. 
     
     
         12 . The method of  claim 1 , wherein the unsupervised clustering model is an unsupervised neural network model. 
     
     
         13 . The method of  claim 12 , wherein the unsupervised neural network model is trained using a set of known CRISPR loci that include spacer and repeat elements of the known CRISPR loci. 
     
     
         14 . The method of  claim 1 , wherein the one or more biological functions of the putative novel effector are screened for activity against one or more loci associated with a disease. 
     
     
         15 . The method of  claim 14 , one or more loci are selected from the group consisting of tumor antigen selected from human telomerase reverse transcriptase (hTERT), survivin, mouse double minute 2 homolog (MDM2), cytochrome P450 1B 1 (CYP1B), HER2/neu, Wilms' tumor gene 1 (WT1), livin, alphafetoprotein (AFP), carcinoembryonic antigen (CEA), mucin 16 (MUC16), MUC1, prostate-specific membrane antigen (PSMA), p53 or cyclin (DI). 
     
     
         16 . The method of  claim 14 , one or more loci are selected from the group consisting of B cell maturation antigen (BCMA), transmembrane activator and CAML Interactor (TACI), or B-cell activating factor receptor (BAFF-R), CD19, PD-1, CD38, CD138, CS-1, CD33, CD26, CD30, CD53, CD70, CD92, CD100, CD148, CD150, CD200, CD261, CD262, or CD362. 
     
     
         17 . The method of  claim 14 , wherein the one or more loci is transthyretin (TTR) 
     
     
         18 . The method of  claim 14 , wherein the one or more loci is PCSK9. 
     
     
         19 . The method of  claim 14 , wherein the one or more loci is BCL11A.