/bio-genome-engineering-prime-editing-design

name: bio-genome-engineering-prime-editing-design
description: Design pegRNAs for prime editing using PrimeDesign algorithms. Generate spacer, PBS, and RT template sequences for precise genomic modifications without double-strand breaks. Use when designing prime editing experiments for precise insertions, deletions, or point mutations.
tool_type: python
primary_tool: PrimeDesign

Version Compatibility

Reference examples tested with: BioPython 1.83+

Before using code patterns, verify installed versions match. If versions differ:

• Python: `pip show <package>` then `help(module.function)` to check signatures

If code throws ImportError, AttributeError, or TypeError, introspect the installed package and adapt the example to match the actual API rather than retrying.

Prime Editing Design

**"Design a prime editing guide for my point mutation"** → Generate pegRNA sequences (spacer, scaffold, RT template, PBS) for precise genomic modifications without double-strand breaks, optimizing PBS length and RT template for editing efficiency.

• Python: PrimeDesign algorithms with `Bio.Seq` for sequence handling

pegRNA Structure

pegRNA components:
1. Spacer (20nt) - guides Cas9 to target site
2. Scaffold - Cas9 binding sequence
3. RT template - encodes the desired edit
4. PBS (primer binding site) - anneals to nicked strand

        Spacer (20nt)      Scaffold     RT template    PBS
    5'─[NNNNNNNNNNNNNNNNNNNN]─[scaffold]─[edit]─────[PBS]─3'

Design pegRNA for Point Mutation

from Bio.Seq import Seq

def design_pegrna_substitution(target_seq, edit_pos, new_base, pbs_length=13, rt_length=15):
    '''Design pegRNA for a point mutation

    Args:
        target_seq: ~100bp sequence centered on edit site
        edit_pos: Position of nucleotide to change (0-indexed in target_seq)
        new_base: New nucleotide (A, C, G, or T)
        pbs_length: Primer binding site length (13-17nt optimal)
                   Shorter = less stable, Longer = more secondary structure
        rt_length: RT template length including edit (10-20nt for substitutions)

    Returns:
        dict with pegRNA components
    '''
    target_seq = target_seq.upper()

    # Find nick site (3bp upstream of PAM, which is 3bp after edit for +strand)
    # For substitution, nick should be close to edit site
    nick_pos = edit_pos + 3  # Adjust based on PAM location

    # Spacer: 20nt upstream of PAM
    spacer_start = nick_pos - 17  # Nick is 3bp upstream of PAM
    spacer = target_seq[spacer_start:spacer_start + 20]

    # PBS: Reverse complement of sequence just upstream of nick
    pbs_region = target_seq[nick_pos - pbs_length:nick_pos]
    pbs = str(Seq(pbs_region).reverse_complement())

    # RT template: Contains the edit
    # Sequence from nick site, with edit incorporated
    rt_region = list(target_seq[nick_pos:nick_pos + rt_length])

    # Incorporate the edit
    edit_offset = edit_pos - nick_pos
    if 0 <= edit_offset < len(rt_region):
        rt_region[edit_offset] = new_base

    rt_template = str(Seq(''.join(rt_region)).reverse_complement())

    return {
        'spacer': spacer,
        'pbs': pbs,
        'rt_template': rt_template,
        'pbs_length': pbs_length,
        'rt_length': rt_length,
        'edit_type': 'substitution'
    }

PBS Length Optimization

def optimize_pbs_length(nick_region, min_len=10, max_len=17):
    '''Find optimal PBS length

    PBS considerations:
    - Too short (<10nt): Unstable annealing, low editing efficiency
    - Too long (>17nt): Secondary structure, reduced efficiency
    - Optimal: 13-17nt with 40-60% GC content

    Returns list of PBS options with predicted stability
    '''
    options = []

    for length in range(min_len, max_len + 1):
        pbs_region = nick_region[-length:]
        pbs = str(Seq(pbs_region).reverse_complement())

        gc = sum(1 for nt in pbs if nt in 'GC') / length

        # Estimate melting temperature (simplified)
        # Tm = 2*(A+T) + 4*(G+C) for short oligos
        at = sum(1 for nt in pbs if nt in 'AT')
        gc_count = length - at
        tm = 2 * at + 4 * gc_count

        # Score based on optimal parameters
        score = 1.0
        if gc < 0.4 or gc > 0.6:
            score -= 0.2
        if tm < 45 or tm > 65:
            score -= 0.2
        if length < 13:
            score -= 0.1

        options.append({
            'length': length,
            'sequence': pbs,
            'gc_content': gc,
            'melting_temp': tm,
            'score': score
        })

    return sorted(options, key=lambda x: x['score'], reverse=True)

RT Template Design

def design_rt_template(edit_type, target_seq, nick_pos, **edit_params):
    '''Design RT template for different edit types

    Edit types and typical RT lengths:
    - Substitution: 10-20nt (edit near 5' end of RT)
    - Small insertion (<20bp): RT length = 10 + insertion length
    - Small deletion (<20bp): RT length = 15-25nt flanking deletion
    - Large insertion: May require multiple pegRNAs (twinPE)
    '''
    if edit_type == 'substitution':
        new_base = edit_params['new_base']
        edit_offset = edit_params['edit_pos'] - nick_pos
        rt_len = max(15, edit_offset + 5)

        rt_region = list(target_seq[nick_pos:nick_pos + rt_len])
        if 0 <= edit_offset < len(rt_region):
            rt_region[edit_offset] = new_base

        return str(Seq(''.join(rt_region)).reverse_complement())

    elif edit_type == 'insertion':
        insert_seq = edit_params['insert_seq']
        insert_pos = edit_params['insert_pos'] - nick_pos

        # Build RT with insertion
        rt_5prime = target_seq[nick_pos:nick_pos + insert_pos]
        rt_3prime = target_seq[nick_pos + insert_pos:nick_pos + insert_pos + 10]

        rt_region = rt_5prime + insert_seq + rt_3prime
        return str(Seq(rt_region).reverse_complement())

    elif edit_type == 'deletion':
        del_start = edit_params['del_sta