Design guide sequences for C-to-T or A-to-G base editing by comparing target positions, nearby bases that may also change, and sequence context. Includes Python examples for ranking candidates and considering paired edits.
Design guides for cytosine and adenine base editing using editing window optimization and BE-Hive outcome prediction. Select optimal positions for C-to-T or A-to-G conversions without double-strand breaks. Use when designing base editor experiments for precise nucleotide changes.
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Reference examples tested with: BioPython 1.83+
Before using code patterns, verify installed versions match. If versions differ:
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"Design a base editor guide for my C-to-T conversion" → Identify guide sequences that position the target nucleotide within the editing window of cytosine (CBE) or adenine (ABE) base editors, predicting editing outcomes and bystander effects.
Bio.Seq, BE-Hive outcome predictionCytosine Base Editors (CBE):
- Convert C to T (or G to A on opposite strand)
- Examples: BE3, BE4, BE4max, AncBE4max
- Editing window: Positions 4-8 (PAM-distal numbering)
Adenine Base Editors (ABE):
- Convert A to G (or T to C on opposite strand)
- Examples: ABE7.10, ABE8e, ABE8.20
- Editing window: Positions 4-7 (narrower than CBE)
Position numbering:
Position 1 = PAM-proximal (next to NGG)
Position 20 = PAM-distal (5' end of spacer)
Editing window is typically positions 4-8 from PAM-distal end
Goal: Identify guide sequences that place a target nucleotide within the base editor's editing window while minimizing bystander edits.
Approach: Scan for PAM sites in both orientations, calculate where the target base falls within the spacer, filter guides where the target lands in the CBE (positions 4-8) or ABE (positions 4-7) editing window, and rank by fewest bystander bases in the window.
from Bio.Seq import Seq
import re
# Editing window positions (1-indexed from PAM-distal end)
# Position 1 is first nt of spacer, position 20 is adjacent to PAM
CBE_WINDOW = (4, 8) # BE4max optimal window
ABE_WINDOW = (4, 7) # ABE8e optimal window
def find_cbe_targets(sequence, target_c_position):
'''Find guides that place a C in the CBE editing window
Args:
sequence: DNA sequence containing the target C
target_c_position: 0-indexed position of C to edit
Returns:
List of guide options with editing predictions
'''
sequence = sequence.upper()
guides = []
# Search for PAMs that would place target C in window
for pam_match in re.finditer(r'(?=(.GG))', sequence):
pam_pos = pam_match.start()
# Calculate where target C falls in the spacer
spacer_start = pam_pos - 20
if spacer_start < 0:
continue
c_position_in_spacer = target_c_position - spacer_start + 1 # 1-indexed
# Check if C is in editing window
if CBE_WINDOW[0] <= c_position_in_spacer <= CBE_WINDOW[1]:
spacer = sequence[spacer_start:pam_pos]
# Find bystander Cs in window (may also be edited)
bystanders = []
for i in range(CBE_WINDOW[0] - 1, CBE_WINDOW[1]):
if i < len(spacer) and spacer[i] == 'C' and (spacer_start + i) != target_c_position:
bystanders.append(i + 1)
guides.append({
'spacer': spacer,
'pam_position': pam_pos,
'target_position_in_spacer': c_position_in_spacer,
'bystander_cs': bystanders,
'bystander_count': len(bystanders),
'strand': '+'
})
# Sort by fewest bystanders
return sorted(guides, key=lambda x: x['bystander_count'])
def find_abe_targets(sequence, target_a_position):
'''Find guides that place an A in the ABE editing window'''
sequence = sequence.upper()
guides = []
for pam_match in re.finditer(r'(?=(.GG))', sequence):
pam_pos = pam_match.start()
spacer_start = pam_pos - 20
if spacer_start < 0:
continue
a_position_in_spacer = target_a_position - spacer_start + 1
if ABE_WINDOW[0] <= a_position_in_spacer <= ABE_WINDOW[1]:
spacer = sequence[spacer_start:pam_pos]
bystanders = []
for i in range(ABE_WINDOW[0] - 1, ABE_WINDOW[1]):
if i < len(spacer) and spacer[i] == 'A' and (spacer_start + i) != target_a_position:
bystanders.append(i + 1)
guides.append({
'spacer': spacer,
'pam_position': pam_pos,
'target_position_in_spacer': a_position_in_spacer,
'bystander_as': bystanders,
'bystander_count': len(bystanders),
'strand': '+'
})
return sorted(guides, key=lambda x: x['bystander_count'])
# Position-dependent editing efficiency
# Based on BE-Hive and published data
# Values represent relative editing efficiency (1.0 = maximum)
CBE_POSITION_EFFICIENCY = {
# Position: efficiency (BE4max)
1: 0.05, 2: 0.10, 3: 0.20,
4: 0.70, 5: 0.90, 6: 1.00, # Peak efficiency
7: 0.85, 8: 0.50,
9: 0.20, 10: 0.10
}
ABE_POSITION_EFFICIENCY = {
# Position: efficiency (ABE8e)
1: 0.02, 2: 0.05, 3: 0.15,
4: 0.60, 5: 0.95, 6: 1.00, # Peak at 5-6
7: 0.70,
8: 0.20, 9: 0.05
}
def predict_editing_efficiency(guide, editor='CBE'):
'''Predict editing efficiency based on position
Interpretation:
- >0.7: High efficiency expected (good candidate)
- 0.4-0.7: Moderate efficiency
- <0.4: Low efficiency (consider alternatives)
'''
pos = guide['target_position_in_spacer']
if editor == 'CBE':
efficiency = CBE_POSITION_EFFICIENCY.get(pos, 0.05)
else: # ABE
efficiency = ABE_POSITION_EFFICIENCY.get(pos, 0.05)
return efficiency
def predict_bystander_edits(spacer, editor='CBE'):
'''Predict which bases in the window will be edited
Bystanders are non-target bases in the editing window
that may also be converted. This is a key consideration
for base editing design.
Returns:
List of predicted edits with efficiency scores
'''
edits = []
if editor == 'CBE':
window = CBE_WINDOW
target_base = 'C'
efficiency_map = CBE_POSITION_EFFICIENCY
else:
window = ABE_WINDOW
target_base = 'A'
efficiency_map = ABE_POSITION_EFFICIENCY
for i in range(window[0] - 1, window[1]):
if i < len(spacer) and spacer[i] == target_base:
pos = i + 1 # 1-indexed
edits.append({
'position': pos,
'original': target_base,
'edited': 'T' if editor == 'CBE' else 'G',
'efficiency': efficiency_map.get(pos, 0.1)
})
return edits
def design_dual_edit(sequence, c_position, a_position, max_distance=50):
'''Design for simultaneous C>T and A>G edits
Some applications require both CBE and ABE edits.
This finds guides where both targets are accessible.
'''
cbe_guides = find_cbe_targets(sequence, c_position)
abe_guides = find_abe_targets(sequence, a_position)
# Find compatible pairs (different PAMs, both in window)
compatible = []
for cbe in cbe_guides:
for abe in abe_guides:
distance = abs(cbe['pam_position'] - abe['pam_position'])
if distance > 0 and distance <= max_distance:
compatible.append({
'cbe_guide': cbe,
'abe_guide': abe,
'distance': distance
})
return compatible
def score_sequence_context(spacer, position, editor='CBE'):
'''Score based on sequence context preferences
CBE context preferences (5' neighbor of target C):
- TC: High efficiency (most preferred)
- CC: Good efficiency
- AC: Moderate efficiency
- GC: Lower efficiency
ABE has less pronounced context preferences.
'''
if position < 2 or position > len(spacer):
return 0.5
idx = position - 1 # 0-indexed
if editor == 'CBE':
if idx > 0:
context = spacer[idx - 1]
context_scores = {'T': 1.0, 'C': 0.8, 'A': 0.6, 'G': 0.4}
return context_scores.get(context, 0.5)
else: # ABE
# ABE is less context-dependent
return 0.8
return 0.5
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