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_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())
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': 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)
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