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,
        : rt_length,
        : 
    }

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,
            : pbs,
            : gc,
            : tm,
            : score
        })

     (options, key= x: x[], reverse=)

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_start'] - nick_pos
        del_end = edit_params['del_end'] - nick_pos

        # Skip deleted region in RT
        rt_5prime = target_seq[nick_pos:nick_pos + del_start]
        rt_3prime = target_seq[nick_pos + del_end:nick_pos + del_end + 15]

        rt_region = rt_5prime + rt_3prime
         (Seq(rt_region).reverse_complement())

PE3 Nicking Guide Design

Goal: Design a second nicking guide for the PE3 prime editing strategy to improve editing efficiency by nicking the non-edited strand.

Approach: Search for PAM sites 40-100bp from the pegRNA nick site on the opposite strand, score candidates by proximity to the optimal 50-80bp distance, and return ranked options.

def design_pe3_nick_guide(target_seq, pegrna_nick_pos, edit_pos):
    '''Design second nicking guide for PE3 strategy

    PE3 uses a second nick on the non-edited strand to improve efficiency.
    Nick distance considerations:
    - Too close (<40bp): Increases indel frequency
    - Optimal (40-100bp): Balances efficiency and precision
    - Too far (>100bp): Reduced benefit

    The second nick should be on the opposite strand.
    '''
    # Search for PAM sites 40-100bp from pegRNA nick
    candidates = []

    for offset in range(40, 101):
        # Check downstream
        pos = pegrna_nick_pos + offset
        if pos + 23 <= len(target_seq):
            if target_seq[pos + 21:pos + 23] == 'GG':
                spacer = target_seq[pos:pos + 20]
                candidates.append({
                    'spacer': spacer,
                    'position': pos,
                    'distance': offset,
                    'strand': '+',
                    'relative': 'downstream'
                })

        # Check upstream (reverse complement)
        pos = pegrna_nick_pos - offset
        if pos >= 20:
            rc_check = str(Seq(target_seq[pos - 3:pos + 20]).reverse_complement())
            if rc_check[:2] == 'CC':  # GG on reverse strand
                spacer = str(Seq(target_seq[pos:pos + 20]).reverse_complement())
                candidates.append({
                    : spacer,
                    : pos,
                    : offset,
                    : ,
                    : 
                })

    
     c  candidates:
        c[] =  - (c[] - ) / 

     (candidates, key= x: x[], reverse=)

Complete pegRNA Assembly

# Standard scaffold sequence for SpCas9
CAS9_SCAFFOLD = 'GTTTTAGAGCTAGAAATAGCAAGTTAAAATAAGGCTAGTCCGTTATCAACTTGAAAAAGTGGCACCGAGTCGGTGC'

def assemble_pegrna(spacer, scaffold, rt_template, pbs):
    '''Assemble full pegRNA sequence for ordering

    Components in 5' to 3' order:
    1. Spacer (20nt)
    2. Scaffold (~76nt for SpCas9)
    3. RT template (variable)
    4. PBS (13-17nt)

    For U6 promoter expression, add G at 5' end if spacer doesn't start with G
    '''
    # Add 5' G if needed for U6 transcription
    if not spacer.startswith('G'):
        spacer = 'G' + spacer[1:]  # Replace first nt or add G

    pegrna = spacer + scaffold + rt_template + pbs

    return {
        'full_sequence': pegrna,
        'length': len(pegrna),
        'spacer': spacer,
        'rt_template': rt_template,
        'pbs': pbs
    }

Related Skills

• genome-engineering/grna-design - Standard guide design for comparison
• genome-engineering/base-editing-design - Alternative for C/G to T/A changes
• variant-calling/variant-annotation - Identify pathogenic variants to correct