Design guide RNA sequences for prime editing, a method for precise DNA changes. Generate components for substitutions, small insertions, and deletions, compare primer binding lengths, and assemble complete sequences for experimental planning.
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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.
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Reference examples tested with: BioPython 1.83+
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"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.
Bio.Seq for sequence handlingpegRNA 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'
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'
}
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)
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
return str(Seq(rt_region).reverse_complement())
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': spacer,
'position': pos,
'distance': offset,
'strand': '-',
'relative': 'upstream'
})
# Prefer nicks 50-80bp away
for c in candidates:
c['score'] = 1.0 - abs(c['distance'] - 65) / 100
return sorted(candidates, key=lambda x: x['score'], reverse=True)
# 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
}
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