Day 6: In Silico Gene Editing: CRISPR-Cas9 Target Selection & sgRNA Optimization

1. Molecular Mechanism of Type II CRISPR-Cas9 Endonucleases

The clustered regularly interspaced short palindromic repeats (CRISPR) and CRISPR-associated protein 9 (Cas9) system from Streptococcus pyogenes (SpCas9) is an RNA-guided endonuclease that mediates targeted double-strand breaks (DSBs) in genomic DNA. The system requires two core molecular components:

1. SpCas9 Nuclease: A bi-lobed protein harboring two distinct catalytic endonuclease domains: the RuvC domain (cleaves the non-target strand 3 nucleotides upstream of the PAM) and the HNH domain (cleaves the target strand complementary to the guide RNA).
2. Single Guide RNA (sgRNA): A synthetic 100-nt chimera uniting the target-specifying 20-nt crRNA (spacer) and the structural tracrRNA scaffold required for Cas9 protein binding.

$$\text{Target DNA: } 5'-\underbrace{\text{N}_{20}}_{\text{Protospacer Target}}-\underbrace{\text{NGG}}_{\text{SpCas9 PAM}}-3'$$

2. The Protospacer Adjacent Motif (PAM) Rule

SpCas9 cannot interrogate or cleave any DNA molecule without initial physical recognition of a Protospacer Adjacent Motif (PAM). For wild-type SpCas9, the invariant canonical PAM is $5'\text{-NGG-}3'$ located on the non-target strand directly adjacent to the 3' terminus of the 20-nucleotide protospacer target.

3. In Silico sgRNA Design Criteria & On-Target Scoring

• GC Content: Optimal sgRNA GC content ranges between 40% and 65%. Extremely low GC reduces R-loop hybridization stability; > 70% GC triggers secondary structure hairpin collapse in the guide RNA.
• Poly-T Termination Signal Elimination: The human U6 RNA polymerase III promoter (commonly used for sgRNA expression plasmids) terminates transcription at four consecutive thymidines (TTTT). Any guide RNA containing TTTT will be prematurely truncated in vivo.
• Avoiding 5' Guanine Deficiencies: The U6 promoter requires a 5' Guanine (G) for efficient transcription initiation. If the 20-nt target does not naturally begin with G, an extra 5' G must be added during oligo cloning.
• Off-Target Risk Minimization: Potential off-target cleavage sites must be evaluated across the host genome using algorithms like Cas-OFFinder or Bowtie to ensure zero off-targets with $\le 2$ mismatches in the seed region.

4. Production Python Script: Automated CRISPR-Cas9 Target Scanner & sgRNA Ranker

#!/usr/bin/env python3
"""
Day 6 Practical: In Silico CRISPR-Cas9 sgRNA Target Generator
Scans sense and antisense strands for NGG PAMs, scores on-target metrics,
and filters out poly-T transcription terminator motifs.
"""

from Bio.Seq import Seq
import re

def scan_crispr_targets(gene_sequence: str):
    """Identify all SpCas9 20-nt protospacers followed by NGG PAM."""
    dna = gene_sequence.upper()
    seq_len = len(dna)
    candidates = []
    
    # 1. Scan Sense Strand: Pattern 20nt + [A/C/G/T]GG
    pattern_sense = re.compile(r"(?=([A-Z]{20}[A-Z]GG))")
    for match in pattern_sense.finditer(dna):
        start = match.start()
        full_hit = dna[start : start + 23]
        protospacer = full_hit[:20]
        pam = full_hit[20:]
        
        candidates.append({
            "strand": "Sense (+)",
            "position": f"{start + 1}..{start + 23}",
            "protospacer": protospacer,
            "pam": pam,
            "gc_percent": round(((protospacer.count('G') + protospacer.count('C')) / 20) * 100, 1),
            "poly_t_terminator": "TTTT" in protospacer,
            "starts_with_g": protospacer.startswith("G")
        })
        
    # 2. Scan Antisense Strand (Reverse Complement)
    rev_dna = str(Seq(dna).reverse_complement())
    for match in pattern_sense.finditer(rev_dna):
        start = match.start()
        full_hit = rev_dna[start : start + 23]
        protospacer = full_hit[:20]
        pam = full_hit[20:]
        
        candidates.append({
            "strand": "Antisense (-)",
            "position": f"RevCompl {start + 1}..{start + 23}",
            "protospacer": protospacer,
            "pam": pam,
            "gc_percent": round(((protospacer.count('G') + protospacer.count('C')) / 20) * 100, 1),
            "poly_t_terminator": "TTTT" in protospacer,
            "starts_with_g": protospacer.startswith("G")
        })
        
    return candidates

if __name__ == "__main__":
    exon_sequence = (
        "ATGGAGGAGCCGCAGTCAGATCCTAGCGTCGAGCCCCCTCTGAGTCAGGAAACATTTTCAGACCTATGGAAAC"
        "TACTTCCTGAAAACAACGTTCTGTCCCCCTTGCCGTCCCAAGCAATGGATGATTTGATGCTGTCCCCGGACGAT"
        "ATTGAACAATGGTTCACTGAAGACCCAGGTCCAGATGAAGCTCCCAGAATGCCAGAGGCTGCTCCCCGCGTGGC"
    )

    guides = scan_crispr_targets(exon_sequence)
    print(f"Total Valid CRISPR-Cas9 Targets Found: {len(guides)}\n")
    for idx, g in enumerate(guides[:5], 1):
        print(f"sgRNA #{idx} | {g['strand']} | Position: {g['position']}")
        print(f"  Protospacer (20nt): 5'-{g['protospacer']}-3' | PAM: {g['pam']}")
        print(f"  GC Content: {g['gc_percent']}% | Poly-T: {g['poly_t_terminator']} | 5'-G: {g['starts_with_g']}")
        
        # Scoring heuristic
        score = 100
        if not (40 <= g['gc_percent'] <= 65): score -= 30
        if g['poly_t_terminator']: score -= 50
        if not g['starts_with_g']: score -= 10
        print(f"  In Silico Score: {score}/100\n")